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NeurIPS 2019: Vancouver, BC, Canada
- Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 - Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim:
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. 1-12 - Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee:
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. 13-23 - Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. 24-34 - Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian D. Reid:
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video. 35-45 - Hyeonwoo Yu, Beomhee Lee:
Zero-shot Learning via Simultaneous Generating and Learning. 46-56 - Brian Lubars, Chenhao Tan:
Ask not what AI can do, but what AI should do: Towards a framework of task delegability. 57-67 - Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens:
Stand-Alone Self-Attention in Vision Models. 68-80 - Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee:
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. 81-91 - Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, Honglak Lee:
Unsupervised learning of object structure and dynamics from videos. 92-102 - Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen:
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. 103-112 - Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. 113-124 - Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Adversarial Examples Are Not Bugs, They Are Features. 125-136 - Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian D. Reid, Hamid Rezatofighi, Silvio Savarese:
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. 137-146 - Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye:
FreeAnchor: Learning to Match Anchors for Visual Object Detection. 147-155 - Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. 156-167 - Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. 168-180 - Mark Bun, Thomas Steinke:
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. 181-191 - Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Alan Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré:
Multi-Resolution Weak Supervision for Sequential Data. 192-203 - Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox:
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. 204-214 - Vladimir V. Kniaz, Vladimir A. Knyaz, Fabio Remondino:
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection. 215-226 - Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. 227-238 - Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan:
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement. 239-249 - Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. 250-260 - Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde:
Generalized Sliced Wasserstein Distances. 261-272 - Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. 273-283 - Sefi Bell-Kligler, Assaf Shocher, Michal Irani:
Blind Super-Resolution Kernel Estimation using an Internal-GAN. 284-293 - Alexandre Louis Lamy, Ziyuan Zhong:
Noise-tolerant fair classification. 294-305 - Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou:
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection. 306-316 - Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang:
Joint-task Self-supervised Learning for Temporal Correspondence. 317-327 - Justin Domke:
Provable Gradient Variance Guarantees for Black-Box Variational Inference. 328-337 - Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. 338-347 - David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne:
Experience Replay for Continual Learning. 348-358 - Boris Hanin, David Rolnick:
Deep ReLU Networks Have Surprisingly Few Activation Patterns. 359-368 - Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee:
Chasing Ghosts: Instruction Following as Bayesian State Tracking. 369-379 - Yu Sun, Jiaming Liu, Ulugbek Kamilov:
Block Coordinate Regularization by Denoising. 380-390 - Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien:
Reducing Noise in GAN Training with Variance Reduced Extragradient. 391-401 - Zihan Li, Matthias Fresacher, Jonathan Scarlett:
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries. 402-412 - Hisham Husain, Richard Nock, Robert C. Williamson:
A Primal-Dual link between GANs and Autoencoders. 413-422 - Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu:
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking. 423-432 - Qiming Zhang, Jing Zhang, Wei Liu, Dacheng Tao:
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. 433-443 - Patrick Putzky, Max Welling:
Invert to Learn to Invert. 444-454 - Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras:
Equitable Stable Matchings in Quadratic Time. 455-465 - Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez:
Zero-Shot Semantic Segmentation. 466-477 - Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray:
Metric Learning for Adversarial Robustness. 478-489 - Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, Ulrich Neumann:
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction. 490-500 - Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou:
Batched Multi-armed Bandits Problem. 501-511 - Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang:
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. 512-522 - Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Linear Regression. 523-533 - Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu:
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos. 534-544 - Bichuan Guo, Yuxing Han, Jiangtao Wen:
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling. 545-556 - Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu:
CPM-Nets: Cross Partial Multi-View Networks. 557-567 - Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, Hongsheng Li:
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis. 568-578 - Andrey Kolobov, Yuval Peres, Cheng Lu, Eric Horvitz:
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling. 579-589 - Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell:
SySCD: A System-Aware Parallel Coordinate Descent Algorithm. 590-600 - Artem Sobolev, Dmitry P. Vetrov:
Importance Weighted Hierarchical Variational Inference. 601-613 - Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik:
RSN: Randomized Subspace Newton. 614-623 - Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan:
Trust Region-Guided Proximal Policy Optimization. 624-634 - Dina Bashkirova, Ben Usman, Kate Saenko:
Adversarial Self-Defense for Cycle-Consistent GANs. 635-645 - Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. 646-656 - Armin Lederer, Jonas Umlauft, Sandra Hirche:
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control. 657-667 - Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang:
ETNet: Error Transition Network for Arbitrary Style Transfer. 668-677 - Max Vladymyrov:
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. 678-687 - Shaojie Bai, J. Zico Kolter, Vladlen Koltun:
Deep Equilibrium Models. 688-699 - Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. 700-712 - Miaoyan Wang, Yuchen Zeng:
Multiway clustering via tensor block models. 713-723 - Wang Chi Cheung:
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives. 724-734 - Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang:
NAT: Neural Architecture Transformer for Accurate and Compact Architectures. 735-747 - Ruidi Chen, Ioannis Ch. Paschalidis:
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression. 748-758 - Xuanyi Dong, Yi Yang:
Network Pruning via Transformable Architecture Search. 759-770 - Junbang Liang, Ming C. Lin, Vladlen Koltun:
Differentiable Cloth Simulation for Inverse Problems. 771-780 - Aaron Schein, Scott W. Linderman, Mingyuan Zhou
, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. 781-792 - Gengshan Yang, Deva Ramanan:
Volumetric Correspondence Networks for Optical Flow. 793-803 - Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu:
Learning Conditional Deformable Templates with Convolutional Networks. 804-816 - Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu:
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data. 817-827 - Zhao Song, Ruosong Wang, Lin F. Yang
Efficient Symmetric Norm Regression via Linear Sketching. 828-838 - Rémi Cadène, Corentin Dancette, Hédi Ben-Younes, Matthieu Cord, Devi Parikh:
RUBi: Reducing Unimodal Biases for Visual Question Answering. 839-850 - Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang:
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition. 851-863 - Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma:
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution. 864-873 - Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan:
DATA: Differentiable ArchiTecture Approximation. 874-884 - Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao:
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge. 885-895 - Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu:
Memory-oriented Decoder for Light Field Salient Object Detection. 896-906 - Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen:
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. 907-917 - Natalia Neverova, David Novotný, Andrea Vedaldi:
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. 918-926 - Chris Wendler, Markus Püschel, Dan Alistarh:
Powerset Convolutional Neural Networks. 927-938 - Arsenii Vanunts, Alexey Drutsa:
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer. 939-951 - Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. 952-962 - Zhijian Liu, Haotian Tang, Yujun Lin, Song Han:
Point-Voxel CNN for Efficient 3D Deep Learning. 963-973 - Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning without Weight Transport. 974-982 - Aadirupa Saha, Aditya Gopalan:
Combinatorial Bandits with Relative Feedback. 983-993 - Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao:
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme. 994-1004 - Leonidas J. Guibas, Qixing Huang, Zhenxiao Liang:
A Condition Number for Joint Optimization of Cycle-Consistent Networks. 1005-1015 - Nicki Skafte Detlefsen, Søren Hauberg:
Explicit Disentanglement of Appearance and Perspective in Generative Models. 1016-1026 - Hédi Hadiji:
Polynomial Cost of Adaptation for X-Armed Bandits. 1027-1036 - Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang
Learning to Propagate for Graph Meta-Learning. 1037-1048 - Sepehr Assadi, Eric Balkanski, Renato Paes Leme:
Secretary Ranking with Minimal Inversions. 1049-1061 - Siqi Liu, Milos Hauskrecht:
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. 1062-1072 - Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu:
Learning Perceptual Inference by Contrasting. 1073-1085 - Yu-Chia Chen, Marina Meila:
Selecting the independent coordinates of manifolds with large aspect ratios. 1086-1095 - Zhengyang Shen, François-Xavier Vialard, Marc Niethammer:
Region-specific Diffeomorphic Metric Mapping. 1096-1106 - Chengguang Xu, Ehsan Elhamifar:
Deep Supervised Summarization: Algorithm and Application to Learning Instructions. 1107-1118 - Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein:
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. 1119-1130 - Brett Daley, Christopher Amato:
Reconciling λ-Returns with Experience Replay. 1131-1140 - Fengxiang He, Tongliang Liu, Dacheng Tao:
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. 1141-1150 - Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. 1151-1160 - Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama:
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. 1161-1170 - Paul Hongsuck Seo, Geeho Kim, Bohyung Han:
Combinatorial Inference against Label Noise. 1171-1181 - Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong:
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. 1182-1191 - Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi:
Convolution with even-sized kernels and symmetric padding. 1192-1203 - Dong Liu, Haochen Zhang, Zhiwei Xiong:
On The Classification-Distortion-Perception Tradeoff. 1204-1213 - Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. 1214-1225 - Holden Lee, Oren Mangoubi, Nisheeth K. Vishnoi:
Online sampling from log-concave distributions. 1226-1237 - Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. 1238-1248 - Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum:
Finding Friend and Foe in Multi-Agent Games. 1249-1259 - Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Image Synthesis with a Single (Robust) Classifier. 1260-1271 - Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu:
Model Compression with Adversarial Robustness: A Unified Optimization Framework. 1283-1294 - Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh:
Cross-channel Communication Networks. 1295-1304 - Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam:
CondConv: Conditionally Parameterized Convolutions for Efficient Inference. 1305-1316 - Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei:
Regression Planning Networks. 1317-1327 - Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxilary Classifiers GAN. 1328-1337 - Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li:
Conditional Structure Generation through Graph Variational Generative Adversarial Nets. 1338-1349 - Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. 1350-1360 - Edith Cohen, Ofir Geri:
Sampling Sketches for Concave Sublinear Functions of Frequencies. 1361-1371 - Pei Wang, Nuno Vasconcelos:
Deliberative Explanations: visualizing network insecurities. 1372-1383 - Eugène Ndiaye, Ichiro Takeuchi:
Computing Full Conformal Prediction Set with Approximate Homotopy. 1384-1393 - Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. 1394-1406 - Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang:
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards. 1407-1417 - Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar:
Multi-View Reinforcement Learning. 1418-1429 - Thang Vu, Hyunjun Jang, Trung X. Pham, Chang Dong Yoo:
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution. 1430-1440 - Jian Sun, Zongben Xu:
Neural Diffusion Distance for Image Segmentation. 1441-1451 - Mete Ozay:
Fine-grained Optimization of Deep Neural Networks. 1452-1462 - Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun:
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. 1463-1473 - Chris Russell, Matteo Toso, Neill D. F. Campbell:
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions. 1474-1484 - Pascal Mettes, Elise van der Pol, Cees Snoek:
Hyperspherical Prototype Networks. 1485-1495 - Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac:
Expressive power of tensor-network factorizations for probabilistic modeling. 1496-1508 - Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar:
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. 1509-1520 - Zhize Li:
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points. 1521-1531 - Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng:
Efficient Meta Learning via Minibatch Proximal Update. 1532-1542 - Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. 1543-1553 - Chundi Liu, Guang Wei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti:
Guided Similarity Separation for Image Retrieval. 1554-1564 - Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. 1565-1576 - Yuan Deng, Jon Schneider, Balasubramanian Sivan:
Strategizing against No-regret Learners. 1577-1585 - Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen:
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs. 1586-1598 - Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon:
Hierarchical Optimal Transport for Document Representation. 1599-1609 - Rui Li:
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes. 1610-1619 - Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge J. Belongie:
Positional Normalization. 1620-1632 - Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger:
A New Defense Against Adversarial Images: Turning a Weakness into a Strength. 1633-1644 - Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang:
Quadratic Video Interpolation. 1645-1654 - Bao Wang, Zuoqiang Shi, Stanley J. Osher:
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. 1655-1665 - Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, Yingli Tian, Jan Ernst, Andreas Hutter:
Incremental Scene Synthesis. 1666-1676 - Shikun Liu, Andrew J. Davison, Edward Johns:
Self-Supervised Generalisation with Meta Auxiliary Learning. 1677-1687 - Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang:
Variational Denoising Network: Toward Blind Noise Modeling and Removal. 1688-1699 - Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima:
Fast Sparse Group Lasso. 1700-1708 - Lin Song, Yanwei Li, Zeming Li, Gang Yu
Learnable Tree Filter for Structure-preserving Feature Transform. 1709-1719 - Yuki Yoshida, Masato Okada:
Data-Dependence of Plateau Phenomenon in Learning with Neural Network - Statistical Mechanical Analysis. 1720-1728 - Talfan Evans, Neil Burgess:
Coordinated hippocampal-entorhinal replay as structural inference. 1729-1741 - Hao Zheng, Faming Fang, Guixu Zhang:
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction. 1742-1752 - Aaron Defazio, Léon Bottou:
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. 1753-1763 - Aaron Defazio:
On the Curved Geometry of Accelerated Optimization. 1764-1773 - Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan:
Multi-marginal Wasserstein GAN. 1774-1784 - Kamil Ciosek, Quan Vuong, Robert Tyler Loftin, Katja Hofmann:
Better Exploration with Optimistic Actor Critic. 1785-1796 - Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White:
Importance Resampling for Off-policy Prediction. 1797-1807 - Songbai Yan, Kamalika Chaudhuri, Tara Javidi
The Label Complexity of Active Learning from Observational Data. 1808-1817 - Khurram Javed, Martha White:
Meta-Learning Representations for Continual Learning. 1818-1828 - Haichao Zhang, Jianyu Wang:
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training. 1829-1839 - Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. 1840-1851 - Alex X. Lu, Amy X. Lu, Wiebke Schormann, Marzyeh Ghassemi, David W. Andrews, Alan M. Moses:
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. 1852-1860 - Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen:
Nonconvex Low-Rank Tensor Completion from Noisy Data. 1861-1872 - Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman:
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization. 1873-1883 - Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh:
Channel Gating Neural Networks. 1884-1894 - Guruprasad Raghavan, Matt Thomson:
Neural networks grown and self-organized by noise. 1895-1905 - Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. 1906-1916 - Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng:
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting. 1917-1928 - Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang:
Variational Structured Semantic Inference for Diverse Image Captioning. 1929-1939 - Zhiao Huang, Fangchen Liu, Hao Su:
Mapping State Space using Landmarks for Universal Goal Reaching. 1940-1950 - Ximei Wang, Ying Jin, Mingsheng Long
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. 1951-1961 - Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd:
Random deep neural networks are biased towards simple functions. 1962-1974 - Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor:
XNAS: Neural Architecture Search with Expert Advice. 1975-1985 - Wei-Da Chen, Shan-Hung Wu:
CNN2: Viewpoint Generalization via a Binocular Vision. 1986-1998 - Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Generalized Off-Policy Actor-Critic. 1999-2009 - Shangtong Zhang, Shimon Whiteson:
DAC: The Double Actor-Critic Architecture for Learning Options. 2010-2020 - Tao Yu, Christopher De Sa:
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models. 2021-2031 - Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman:
Controlling Neural Level Sets. 2032-2041 - Cyrille W. Combettes, Sebastian Pokutta:
Blended Matching Pursuit. 2042-2052 - Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. 2053-2062 - Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr:
Controllable Text-to-Image Generation. 2063-2073 - Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin:
Improving Textual Network Learning with Variational Homophilic Embeddings. 2074-2085 - Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng:
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. 2086-2097 - Ruoqi Shen, Yin Tat Lee:
The Randomized Midpoint Method for Log-Concave Sampling. 2098-2109 - Su Young Lee, Sung-Ik Choi, Sae-Young Chung:
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update. 2110-2119 - Takahiro Omi, Naonori Ueda, Kazuyuki Aihara:
Fully Neural Network based Model for General Temporal Point Processes. 2120-2129 - Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang:
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks. 2130-2141 - Faidra Georgia Monachou, Itai Ashlagi:
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design. 2142-2152 - Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman:
Provably Powerful Graph Networks. 2153-2164 - Arsalan Sharif-Nassab, Saber Salehkaleybar, S. Jamaloddin Golestani:
Order Optimal One-Shot Distributed Learning. 2165-2174 - Jie Hu, Rongrong Ji, Shengchuan Zhang, Xiaoshuai Sun, Qixiang Ye, Chia-Wen Lin, Qi Tian:
Information Competing Process for Learning Diversified Representations. 2175-2186 - Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. 2187-2198 - Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. 2199-2208 - Aviv Rosenberg, Yishay Mansour:
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function. 2209-2218 - Xiangyu Zheng, Song Xi Chen:
Partitioning Structure Learning for Segmented Linear Regression Trees. 2219-2228 - Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song:
A Tensorized Transformer for Language Modeling. 2229-2239 - Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. 2240-2250 - Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. 2251-2260 - Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David D. Cox:
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation. 2261-2270 - Dror Simon, Michael Elad:
Rethinking the CSC Model for Natural Images. 2271-2281 - Weishi Shi, Qi Yu
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning. 2282-2291 - Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros:
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. 2292-2302 - Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann:
Perceiving the arrow of time in autoregressive motion. 2303-2314 - Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. 2315-2325 - Eliya Nachmani, Lior Wolf:
Hyper-Graph-Network Decoders for Block Codes. 2326-2336 - Adrian Rivera Cardoso, He Wang, Huan Xu:
Large Scale Markov Decision Processes with Changing Rewards. 2337-2347 - Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas:
Multiview Aggregation for Learning Category-Specific Shape Reconstruction. 2348-2359 - Virag Shah, Ramesh Johari, Jose H. Blanchet:
Semi-Parametric Dynamic Contextual Pricing. 2360-2370 - Alan Kuhnle:
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time. 2371-2381 - Rebekka Burkholz, Alina Dubatovka:
Initialization of ReLUs for Dynamical Isometry. 2382-2392 - Jie Ding, A. Robert Calderbank, Vahid Tarokh:
Gradient Information for Representation and Modeling. 2393-2402 - Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. 2403-2413 - Xiyang Liu, Sewoong Oh:
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases. 2414-2425 - Ayan Chakrabarti, Benjamin Moseley:
Backprop with Approximate Activations for Memory-efficient Network Training. 2426-2435 - Zhihao Xia, Ayan Chakrabarti:
Training Image Estimators without Image Ground Truth. 2436-2446 - Lisha Chen, Hui Su, Qiang Ji:
Deep Structured Prediction for Facial Landmark Detection. 2447-2457 - Xiuyuan Lu, Benjamin Van Roy:
Information-Theoretic Confidence Bounds for Reinforcement Learning. 2458-2466 - Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Transfer Anomaly Detection by Inferring Latent Domain Representations. 2467-2477 - Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang:
Total Least Squares Regression in Input Sparsity Time. 2478-2489 - Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh:
Park: An Open Platform for Learning-Augmented Computer Systems. 2490-2502 - Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. 2503-2513 - Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. 2514-2525 - Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas:
Conformal Prediction Under Covariate Shift. 2526-2536 - Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar:
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. 2537-2548 - Haowei He, Gao Huang, Yang Yuan:
Asymmetric Valleys: Beyond Sharp and Flat Local Minima. 2549-2560 - Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu:
Positive-Unlabeled Compression on the Cloud. 2561-2570 - Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
Direct Estimation of Differential Functional Graphical Models. 2571-2581 - Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama:
On the Calibration of Multiclass Classification with Rejection. 2582-2592 - Pratyusha Sharma, Deepak Pathak, Abhinav Gupta:
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller. 2593-2603 - Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang:
Stagewise Training Accelerates Convergence of Testing Error Over SGD. 2604-2614 - Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka:
Learning Robust Options by Conditional Value at Risk Optimization. 2615-2625 - Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. 2626-2636 - Lili Su, Pengkun Yang:
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective. 2637-2646 - Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez:
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries. 2647-2657 - Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He:
Dual Variational Generation for Low Shot Heterogeneous Face Recognition. 2670-2679 - Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari:
Discovering Neural Wirings. 2680-2690 - Baekjin Kim, Ambuj Tewari:
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. 2691-2700 - Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang:
Knowledge Extraction with No Observable Data. 2701-2710 - Matthew J. Holland:
PAC-Bayes under potentially heavy tails. 2711-2720 - Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu:
One-Shot Object Detection with Co-Attention and Co-Excitation. 2721-2730 - Shuai Zhang, Yi Tay, Lina Yao, Qi Liu:
Quaternion Knowledge Graph Embeddings. 2731-2741 - Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li:
Glyce: Glyph-vectors for Chinese Character Representations. 2742-2753 - Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels. 2754-2764 - Weijiang Yu, Jingwen Zhou, Weihao Yu, Xiaodan Liang, Nong Xiao:
Heterogeneous Graph Learning for Visual Commonsense Reasoning. 2765-2775 - Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. 2776-2787 - Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann:
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. 2788-2799 - Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Identifying Causal Effects via Context-specific Independence Relations. 2800-2810 - Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, Zhi-Hua Zhou:
Bridging Machine Learning and Logical Reasoning by Abductive Learning. 2811-2822 - Zihan Zhang, Xiangyang Ji:
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function. 2823-2832 - Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle:
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. 2833-2843 - Sulaiman A. Alghunaim, Kun Yuan, Ali H. Sayed:
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization. 2844-2854 - Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola:
Regularizing Trajectory Optimization with Denoising Autoencoders. 2855-2865 - Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. 2866-2875 - Sivan Sabato:
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits. 2876-2886 - Matteo Turchetta, Felix Berkenkamp, Andreas Krause:
Safe Exploration for Interactive Machine Learning. 2887-2897 - Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez:
Addressing Failure Prediction by Learning Model Confidence. 2898-2909 - ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using the Graph Cartesian Product. 2910-2920 - Juyeon Heo, Sunghwan Joo, Taesup Moon:
Fooling Neural Network Interpretations via Adversarial Model Manipulation. 2921-2932 - Lénaïc Chizat, Edouard Oyallon, Francis R. Bach:
On Lazy Training in Differentiable Programming. 2933-2943 - Parimala Kancharla, Sumohana S. Channappayya:
Quality Aware Generative Adversarial Networks. 2944-2954 - Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. 2955-2967 - Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. 2968-2979 - Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. 2980-2989 - Xiaoming Yu, Yuanqi Chen, Shan Liu, Thomas H. Li, Ge Li:
Multi-mapping Image-to-Image Translation via Learning Disentanglement. 2990-2999 - Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda:
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs. 3000-3010 - Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhi-Hong Deng:
Fast Structured Decoding for Sequence Models. 3011-3020 - Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani:
Learning Temporal Pose Estimation from Sparsely-Labeled Videos. 3021-3032 - Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling:
Putting An End to End-to-End: Gradient-Isolated Learning of Representations. 3033-3045 - Hongteng Xu, Dixin Luo, Lawrence Carin:
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching. 3046-3056 - Satoshi Tsutsui, Yanwei Fu, David J. Crandall:
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition. 3057-3066 - Simon Ramstedt, Chris Pal:
Real-Time Reinforcement Learning. 3067-3076 - Alexander Peysakhovich, Christian Kroer, Adam Lerer:
Robust Multi-agent Counterfactual Prediction. 3077-3087 - Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa:
Approximate Inference Turns Deep Networks into Gaussian Processes. 3088-3098 - Patrick Kidger, Patric Bonnier, Imanol Pérez Arribas, Cristopher Salvi, Terry J. Lyons:
Deep Signature Transforms. 3099-3109 - Yogev Bar-On, Yishay Mansour:
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits. 3110-3120 - Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Convergent Policy Optimization for Safe Reinforcement Learning. 3121-3133 - Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. 3134-3144 - Min-hwan Oh, Garud Iyengar:
Thompson Sampling for Multinomial Logit Contextual Bandits. 3145-3155 - Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann:
Backpropagation-Friendly Eigendecomposition. 3156-3164 - Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu:
FastSpeech: Fast, Robust and Controllable Text to Speech. 3165-3174 - Giovanni Chierchia, Benjamin Perret:
Ultrametric Fitting by Gradient Descent. 3175-3186 - Hanrui Zhang, Yu Cheng, Vincent Conitzer:
Distinguishing Distributions When Samples Are Strategically Transformed. 3187-3195 - Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks. 3196-3206 - Yan Zhang, Jonathon S. Hare, Adam Prügel-Bennett:
Deep Set Prediction Networks. 3207-3217 - Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek:
DppNet: Approximating Determinantal Point Processes with Deep Networks. 3218-3229 - Sai Qian Zhang, Qi Zhang, Jieyu Lin:
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control. 3230-3239 - Ya-Chien Chang, Nima Roohi, Sicun Gao:
Neural Lyapunov Control. 3240-3249 - Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn:
Fully Dynamic Consistent Facility Location. 3250-3260 - Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman:
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. 3261-3275 - Jiaqi Ma
A Flexible Generative Framework for Graph-based Semi-supervised Learning. 3276-3285 - Georgios Detorakis, Sourav Dutta, Abhishek Khanna, Matthew Jerry, Suman Datta, Emre Neftci:
Inherent Weight Normalization in Stochastic Neural Networks. 3286-3297 - Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi:
Optimal Decision Tree with Noisy Outcomes. 3298-3308 - Eunbyung Park, Junier B. Oliva:
Meta-Curvature. 3309-3319 - Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. 3320-3329 - Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai:
KerGM: Kernelized Graph Matching. 3330-3341 - Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, Samy Bengio:
Transfusion: Understanding Transfer Learning for Medical Imaging. 3342-3352 - Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial training for free! 3353-3364 - Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang:
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. 3365-3375 - Vaishak Belle, Brendan Juba:
Implicitly learning to reason in first-order logic. 3376-3386 - Kevin J. Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin:
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods. 3387-3398 - Yongkai Wu
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. 3399-3409 - Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang:
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration. 3410-3420 - Nathan Kallus, Angela Zhou:
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. 3421-3432 - Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. 3433-3443 - Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein:
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models. 3444-3456 - Pierre C. Bellec, Arun K. Kuchibhotla:
First order expansion of convex regularized estimators. 3457-3468 - Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. 3469-3478 - Trevor Campbell, Xinglong Li:
Universal Boosting Variational Inference. 3479-3490 - Dimitris Kalimeris, Gal Kaplun, Preetum Nakkiran, Benjamin L. Edelman, Tristan Yang, Boaz Barak, Haofeng Zhang:
SGD on Neural Networks Learns Functions of Increasing Complexity. 3491-3501 - Shuang Li, Gongguo Tang, Michael B. Wakin:
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk. 3502-3512 - Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou:
Making AI Forget You: Data Deletion in Machine Learning. 3513-3526 - David Durfee, Ryan M. Rogers:
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. 3527-3537 - Yaniv Romano, Evan Patterson, Emmanuel J. Candès:
Conformalized Quantile Regression. 3538-3548 - Seungki Min, Costis Maglaras, Ciamac C. Moallemi:
Thompson Sampling with Information Relaxation Penalties. 3549-3558 - Andrew Bennett, Nathan Kallus, Tobias Schnabel:
Deep Generalized Method of Moments for Instrumental Variable Analysis. 3559-3569 - Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. 3570-3580 - Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz:
Dancing to Music. 3581-3591 - Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. 3592-3602 - Yilun Du, Igor Mordatch:
Implicit Generation and Modeling with Energy Based Models. 3603-3613 - Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. 3614-3624 - Christopher Thomas, Adriana Kovashka:
Predicting the Politics of an Image Using Webly Supervised Data. 3625-3637 - Lingyu Liang, Lianwen Jin, Yong Xu:
Adaptive GNN for Image Analysis and Editing. 3638-3649 - Tavor Z. Baharav, David Tse:
Ultra Fast Medoid Identification via Correlated Sequential Halving. 3650-3659 - Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk:
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD. 3660-3669 - Edgar Dobriban, Sifan Liu:
Asymptotics for Sketching in Least Squares Regression. 3670-3680 - Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. 3681-3692 - Kevin Bello, Jean Honorio:
Exact inference in structured prediction. 3693-3702 - Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar
Coda: An End-to-End Neural Program Decompiler. 3703-3714 - Gunpil Hwang, Seohyeon Kim, Hyeon-Min Bae:
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes. 3715-3726 - Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. 3727-3740 - Dominik Linzner, Michael Schmidt, Heinz Koeppl:
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. 3741-3751 - Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, Anderson C. A. Nascimento:
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. 3752-3764 - Jonathan R. Ullman, Adam Sealfon:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. 3765-3775 - Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell:
Learning Representations for Time Series Clustering. 3776-3786 - Ananya Kumar, Percy Liang, Tengyu Ma:
Verified Uncertainty Calibration. 3787-3798 - Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee:
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits. 3799-3808 - Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim:
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction. 3809-3819 - Yiwen Guo, Ziang Yan, Changshui Zhang:
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks. 3820-3829 - Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. 3830-3841 - Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen:
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. 3842-3851 - Xing Yan, Qi Wu, Wen Zhang:
Cross-sectional Learning of Extremal Dependence among Financial Assets. 3852-3862 - Yujia Jin, Aaron Sidford:
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. 3863-3873 - Jonathan Ho, Evan Lohn, Pieter Abbeel:
Compression with Flows via Local Bits-Back Coding. 3874-3883 - Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg:
Exact Rate-Distortion in Autoencoders via Echo Noise. 3884-3895 - Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao:
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI. 3896-3906 - Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu:
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction. 3907-3917 - Shangyu Chen, Wenya Wang, Sinno Jialin Pan:
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization. 3918-3928 - Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila:
Improved Precision and Recall Metric for Assessing Generative Models. 3929-3938 - Jiajin Li, Sen Huang, Anthony Man-Cho So:
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression. 3939-3949 - Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang:
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph. 3950-3960 - Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. 3961-3972 - Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai:
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. 3973-3982 - Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh:
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. 3983-3993 - Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. 3994-4004 - Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen:
Cross Attention Network for Few-shot Classification. 4005-4016 - Qing Qu, Xiao Li, Zhihui Zhu:
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. 4017-4028 - Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, Kaisheng Ma
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models. 4029-4038 - Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. 4039-4049 - Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
Teaching Multiple Concepts to a Forgetful Learner. 4050-4060 - Frank Ban, David P. Woodruff, Qiuyi (Richard) Zhang:
Regularized Weighted Low Rank Approximation. 4061-4071 - Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. 4072-4082 - Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Approximation Ratios of Graph Neural Networks for Combinatorial Problems. 4083-4092 - Tianbo Li, Yiping Ke:
Thinning for Accelerating the Learning of Point Processes. 4093-4103 - Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak:
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. 4104-4114 - Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. 4115-4125 - Suraj Srinivas, François Fleuret:
Full-Gradient Representation for Neural Network Visualization. 4126-4135 - Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash:
q-means: A quantum algorithm for unsupervised machine learning. 4136-4146 - Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. 4147-4157 - Frederik Kunstner, Philipp Hennig, Lukas Balles:
Limitations of the empirical Fisher approximation for natural gradient descent. 4158-4169 - Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna:
Flow-based Image-to-Image Translation with Feature Disentanglement. 4170-4180 - Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi:
Learning dynamic polynomial proofs. 4181-4190 - Vincent Le Guen, Nicolas Thome:
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models. 4191-4203 - Boris Knyazev, Graham W. Taylor, Mohamed R. Amer:
Understanding Attention and Generalization in Graph Neural Networks. 4204-4214 - Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. 4215-4224 - Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang:
Curvilinear Distance Metric Learning. 4225-4234 - Yaqi Xie, Ziwei Xu, Kuldeep S. Meel, Mohan S. Kankanhalli, Harold Soh:
Embedding Symbolic Knowledge into Deep Networks. 4235-4245 - Raanan Y. Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik:
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections. 4246-4256 - Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel:
Efficient Graph Generation with Graph Recurrent Attention Networks. 4257-4267 - Mahesh Chandra Mukkamala, Peter Ochs:
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. 4268-4278 - Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo:
Learning Deep Bilinear Transformation for Fine-grained Image Representation. 4279-4288 - Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. 4289-4301 - Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack W. Rae:
Training Language GANs from Scratch. 4302-4313 - Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. 4314-4324 - James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. 4325-4334 - Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli:
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters. 4335-4347 - Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal:
On Adversarial Mixup Resynthesis. 4348-4359 - Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. 4360-4371 - Joshua K. Lee, Prasanna Sattigeri, Gregory W. Wornell:
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. 4372-4382 - Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin:
Understanding and Improving Layer Normalization. 4383-4393 - Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon:
Uncertainty-based Continual Learning with Adaptive Regularization. 4394-4404 - Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao:
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning. 4405-4416 - Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel:
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging. 4417-4428 - Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed:
Massively scalable Sinkhorn distances via the Nyström method. 4429-4439 - Yue Yu, Jiaxiang Wu, Longbo Huang:
Double Quantization for Communication-Efficient Distributed Optimization. 4440-4451 - Bryon Aragam, Arash A. Amini, Qing Zhou:
Globally optimal score-based learning of directed acyclic graphs in high-dimensions. 4452-4464 - Ivana Balazevic, Carl Allen, Timothy M. Hospedales:
Multi-relational Poincaré Graph Embeddings. 4465-4475 - Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville:
No-Press Diplomacy: Modeling Multi-Agent Gameplay. 4476-4487 - Yaqi Duan, Zheng Tracy Ke, Mengdi Wang:
State Aggregation Learning from Markov Transition Data. 4488-4497 - Charles T. Marx, Richard L. Phillips, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian:
Disentangling Influence: Using disentangled representations to audit model predictions. 4498-4508 - David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek:
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. 4509-4518 - Théo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, Romain Gay:
Partially Encrypted Deep Learning using Functional Encryption. 4519-4530 - David Martínez-Rubio
Decentralized Cooperative Stochastic Bandits. 4531-4542 - Gonzalo Mena, Jonathan Niles-Weed:
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. 4543-4553 - Shirin Jalali, Carl J. Nuzman, Iraj Saniee:
Efficient Deep Approximation of GMMs. 4554-4562 - Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang:
Learning low-dimensional state embeddings and metastable clusters from time series data. 4563-4572 - Xu Wang, Jingming He, Lin Ma:
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations. 4573-4583 - Creighton Heaukulani, Mark van der Wilk:
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. 4584-4594 - Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. 4595-4607 - Hugo Caselles-Dupré, Michaël Garcia Ortiz, David Filliat:
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. 4608-4617 - Supratik Paul, Vitaly Kurin, Shimon Whiteson:
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods. 4618-4628 - Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen:
Offline Contextual Bayesian Optimization. 4629-4640 - Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson:
Making the Cut: A Bandit-based Approach to Tiered Interviewing. 4641-4651 - Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. 4652-4663 - Tao Tu, John W. Paisley, Stefan Haufe, Paul Sajda:
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. 4664-4673 - Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe:
End to end learning and optimization on graphs. 4674-4685 - Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen:
Game Design for Eliciting Distinguishable Behavior. 4686-4695 - Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When does label smoothing help? 4696-4705 - Harsh Gupta, R. Srikant, Lei Ying:
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning. 4706-4715 - Lixin Fan, Kam Woh Ng, Chee Seng Chan:
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks. 4716-4725 - Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig:
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. 4726-4738 - Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. 4739-4750 - Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos:
Distribution-Independent PAC Learning of Halfspaces with Massart Noise. 4751-4762 - Ronen Basri, David W. Jacobs, Yoni Kasten, Shira Kritchman:
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies. 4763-4772 - Xingyu Lin, Harjatin Singh Baweja, George Kantor, David Held:
Adaptive Auxiliary Task Weighting for Reinforcement Learning. 4773-4784 - Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai:
Blocking Bandits. 4785-4794 - Wei Qian, Yuqian Zhang, Yudong Chen:
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities. 4795-4803 - Yuan Deng, Jon Schneider, Balasubramanian Sivan:
Prior-Free Dynamic Auctions with Low Regret Buyers. 4804-4814 - Taewan Kim, Joydeep Ghosh:
On Single Source Robustness in Deep Fusion Models. 4815-4826 - Andrew Bennett, Nathan Kallus:
Policy Evaluation with Latent Confounders via Optimal Balance. 4827-4837 - Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon:
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting. 4838-4847 - Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro:
Adaptive Cross-Modal Few-shot Learning. 4848-4858 - Ioannis Koutis
Spectral Modification of Graphs for Improved Spectral Clustering. 4859-4868 - Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec:
Hyperbolic Graph Convolutional Neural Networks. 4869-4880 - Shali Jiang, Roman Garnett, Benjamin Moseley:
Cost Effective Active Search. 4881-4890 - Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. 4891-4900 - Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan:
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. 4901-4910 - Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola:
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. 4911-4922 - Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. 4923-4932 - Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. 4933-4943 - Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. 4944-4954 - Suman Kalyan Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani:
Fair Algorithms for Clustering. 4955-4966 - Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang:
Learning Mean-Field Games. 4967-4977 - Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. 4978-4990 - Eric Jonas:
Deep imitation learning for molecular inverse problems. 4991-5001 - Chi Han, Jiayuan Mao, Chuang Gan, Josh Tenenbaum, Jiajun Wu:
Visual Concept-Metaconcept Learning. 5002-5013 - Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz:
Few-shot Video-to-Video Synthesis. 5014-5025 - Weiyang Liu, Zhen Liu, James M. Rehg, Le Song:
Neural Similarity Learning. 5026-5037 - Yikang Shen, Shawn Tan, Seyed Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville:
Ordered Memory. 5038-5049 - David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. 5050-5060 - Jingjing Wang, Sun Sun, Yaoliang Yu:
Multivariate Triangular Quantile Maps for Novelty Detection. 5061-5072 - Sharon Qian, Yaron Singer:
Fast Parallel Algorithms for Statistical Subset Selection Problems. 5073-5082 - Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross B. Girshick:
PHYRE: A New Benchmark for Physical Reasoning. 5083-5094 - Ji Xu, Daniel J. Hsu:
On the number of variables to use in principal component regression. 5095-5104 - Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell:
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery. 5105-5115 - Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski:
Mutually Regressive Point Processes. 5116-5127 - Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda:
Data-driven Estimation of Sinusoid Frequencies. 5128-5138 - Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang:
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings. 5139-5151 - Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros:
ANODEV2: A Coupled Neural ODE Framework. 5152-5162 - Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun:
Estimating Entropy of Distributions in Constant Space. 5163-5174 - Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. 5175-5186 - Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm:
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium. 5187-5197 - Zhengyuan Zhou, Renyuan Xu, Jose H. Blanchet:
Learning in Generalized Linear Contextual Bandits with Stochastic Delays. 5198-5209 - Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans:
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. 5210-5221 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions. 5222-5232 - Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. 5233-5243 - Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan:
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. 5244-5254 - Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang:
On the Accuracy of Influence Functions for Measuring Group Effects. 5255-5265 - Yandong Wen, Bhiksha Raj, Rita Singh:
Face Reconstruction from Voice using Generative Adversarial Networks. 5266-5275 - Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. 5276-5286 - Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
On Testing for Biases in Peer Review. 5287-5297 - Chanho Eom, Bumsub Ham:
Learning Disentangled Representation for Robust Person Re-identification. 5298-5309 - Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei
Balancing Efficiency and Fairness in On-Demand Ridesourcing. 5310-5320 - Yulia Rubanova, Tian Qi Chen, David Duvenaud:
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. 5321-5331 - Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann:
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion. 5332-5342 - Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka:
Input Similarity from the Neural Network Perspective. 5343-5352 - Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi:
Adaptive Sequence Submodularity. 5353-5364 - Adam Gaier, David Ha:
Weight Agnostic Neural Networks. 5365-5379 - C. Daniel Freeman, David Ha, Luke Metz:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. 5380-5391 - Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux:
Reducing the variance in online optimization by transporting past gradients. 5392-5403 - Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor:
Characterizing Bias in Classifiers using Generative Models. 5404-5415 - Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou:
Optimal Stochastic and Online Learning with Individual Iterates. 5416-5426 - Ashudeep Singh, Thorsten Joachims:
Policy Learning for Fairness in Ranking. 5427-5437 - Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine:
Off-Policy Evaluation via Off-Policy Classification. 5438-5449 - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus:
Regularized Gradient Boosting. 5450-5459 - Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. 5460-5473 - Harald Steck:
Markov Random Fields for Collaborative Filtering. 5474-5485 - Edward Raff:
A Step Toward Quantifying Independently Reproducible Machine Learning Research. 5486-5496 - David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. 5497-5508 - Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar:
Time-series Generative Adversarial Networks. 5509-5519 - Qian Yang, Zhouyuan Huo, Wenlin Wang, Heng Huang, Lawrence Carin:
Ouroboros: On Accelerating Training of Transformer-Based Language Models. 5520-5530 - Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou:
A Refined Margin Distribution Analysis for Forest Representation Learning. 5531-5541 - Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. 5542-5552 - Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda:
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks. 5553-5563 - Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. 5564-5574 - Binghui Peng, Wei Chen:
Adaptive Influence Maximization with Myopic Feedback. 5575-5584 - Yiren Zhao, Xitong Gao, Daniel Bates, Robert D. Mullins, Cheng-Zhong Xu:
Focused Quantization for Sparse CNNs. 5585-5594 - Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima P. Karanam, L. Venkata Subramaniam:
Quantum Embedding of Knowledge for Reasoning. 5595-5605 - Vrettos Moulos:
Optimal Best Markovian Arm Identification with Fixed Confidence. 5606-5615 - Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Limiting Extrapolation in Linear Approximate Value Iteration. 5616-5625 - Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill:
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. 5626-5635 - Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth:
Invertible Convolutional Flow. 5636-5646 - Philippe Casgrain:
A Latent Variational Framework for Stochastic Optimization. 5647-5657 - Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen:
Topology-Preserving Deep Image Segmentation. 5658-5669 - Aming Wu, Linchao Zhu
Connective Cognition Network for Directional Visual Commonsense Reasoning. 5670-5680 - Vikas K. Garg, Tamar Pichkhadze:
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms. 5681-5691 - Francisco M. Garcia, Philip S. Thomas:
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. 5692-5701 - Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu:
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently. 5702-5711 - Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu:
Learning Disentangled Representations for Recommendation. 5712-5723 - Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, Keyulu Xu:
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. 5724-5734 - Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim:
In-Place Zero-Space Memory Protection for CNN. 5735-5744 - Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. 5745-5753 - Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. 5754-5764 - Jianghong Shi, Eric Shea-Brown, Michael A. Buice:
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex. 5765-5775 - Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang:
Variance Reduced Policy Evaluation with Smooth Function Approximation. 5776-5787 - Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. 5788-5799 - Tiantian Fang, Alexander G. Schwing:
Co-Generation with GANs using AIS based HMC. 5800-5811 - Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu:
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. 5812-5822 - Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin:
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs. 5823-5833 - Kecheng Zheng, Zheng-Jun Zha, Wei Wei:
Abstract Reasoning with Distracting Features. 5834-5845 - Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang:
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer. 5846-5857 - Florian Tramèr
Adversarial Training and Robustness for Multiple Perturbations. 5858-5868 - Gi-Soo Kim, Myunghee Cho Paik:
Doubly-Robust Lasso Bandit. 5869-5879 - Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang:
DM2C: Deep Mixed-Modal Clustering. 5880-5890 - Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard H. Hovy:
MaCow: Masked Convolutional Generative Flow. 5891-5900 - Drew A. Hudson, Christopher D. Manning:
Learning by Abstraction: The Neural State Machine. 5901-5914 - Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. 5915-5926 - Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang:
Equipping Experts/Bandits with Long-term Memory. 5927-5937 - Wenhao Yang, Xiang Li, Zhihua Zhang:
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. 5938-5948 - Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen:
Scalable inference of topic evolution via models for latent geometric structures. 5949-5959 - Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft:
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. 5960-5973 - Yonatan Geifman, Ran El-Yaniv:
Deep Active Learning with a Neural Architecture Search. 5974-5984 - Chris Criscitiello, Nicolas Boumal:
Efficiently escaping saddle points on manifolds. 5985-5995 - Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon:
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks. 5996-6006 - Asiri Wijesinghe, Qing Wang:
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters. 6007-6018 - Wonjae Kim, Yoonho Lee:
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning. 6019-6030 - Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard:
Comparing Unsupervised Word Translation Methods Step by Step. 6031-6041 - Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao:
Learning from Bad Data via Generation. 6042-6053 - Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi:
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration. 6054-6064 - Yihe Dong, Samuel B. Hopkins, Jerry Li:
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. 6065-6075 - Yanyao Shen, Sujay Sanghavi:
Iterative Least Trimmed Squares for Mixed Linear Regression. 6076-6086 - Yu Qi, Bin Liu, Yueming Wang, Gang Pan:
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces. 6087-6096 - Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang:
Divergence-Augmented Policy Optimization. 6097-6108 - Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan:
Intrinsic dimension of data representations in deep neural networks. 6109-6119 - Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Column Subset Selection. 6120-6131 - Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui:
Compositional De-Attention Networks. 6132-6142 - Jian Ni, Shanghang Zhang, Haiyong Xie:
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning. 6143-6154 - Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang:
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. 6155-6166 - Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin:
Mining GOLD Samples for Conditional GANs. 6167-6178 - Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song:
Deep Model Transferability from Attribution Maps. 6179-6189 - Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. 6190-6199 - Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan:
Direct Optimization through arg max for Discrete Variational Auto-Encoder. 6200-6211 - Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang:
Distributional Reward Decomposition for Reinforcement Learning. 6212-6221 - Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang:
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise. 6222-6233 - Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. 6234-6245 - Dan Zhang, Anna Khoreva:
Progressive Augmentation of GANs. 6246-6256 - Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. 6257-6266 - Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. 6267-6277 - Xinyun Chen, Yuandong Tian:
Learning to Perform Local Rewriting for Combinatorial Optimization. 6278-6289 - Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni:
Anti-efficient encoding in emergent communication. 6290-6300 - Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Singleshot : a scalable Tucker tensor decomposition. 6301-6312 - Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, Tie-Yan Liu:
Neural Machine Translation with Soft Prototype. 6313-6322 - Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg:
Reliable training and estimation of variance networks. 6323-6333 - Weiwei Liu:
Copula Multi-label Learning. 6334-6343 - Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. 6344-6355 - Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. 6356-6367 - Zengfeng Huang
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation. 6368-6378 - Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu:
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks. 6379-6391 - Tomasz Kusmierczyk, Joseph Sakaya, Arto Klami:
Variational Bayesian Decision-making for Continuous Utilities. 6392-6402 - Ryo Karakida, Shotaro Akaho, Shun-ichi Amari:
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks. 6403-6413 - Natasa Tagasovska, David Lopez-Paz:
Single-Model Uncertainties for Deep Learning. 6414-6425 - Eran Malach, Shai Shalev-Shwartz:
Is Deeper Better only when Shallow is Good? 6426-6435 - Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. 6436-6446 - Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, Ben Glocker:
Domain Generalization via Model-Agnostic Learning of Semantic Features. 6447-6458 - Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer:
Grid Saliency for Context Explanations of Semantic Segmentation. 6459-6470 - Ioannis Panageas, Georgios Piliouras, Xiao Wang:
First-order methods almost always avoid saddle points: The case of vanishing step-sizes. 6471-6480 - Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. 6481-6491 - Sajin Sasy, Olga Ohrimenko
Oblivious Sampling Algorithms for Private Data Analysis. 6492-6503 - Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao:
Semi-supervisedly Co-embedding Attributed Networks. 6504-6513 - Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani:
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI. 6514-6524 - Natasa Tagasovska, Damien Ackerer, Thibault Vatter:
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. 6525-6537 - Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin:
Nonstochastic Multiarmed Bandits with Unrestricted Delays. 6538-6547 - Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther:
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. 6548-6558 - Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin:
Code Generation as a Dual Task of Code Summarization. 6559-6569 - Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki, Oren Freifeld:
Diffeomorphic Temporal Alignment Nets. 6570-6581 - Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang:
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior. 6582-6593 - Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. 6594-6604 - Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen:
Efficient Pure Exploration in Adaptive Round model. 6605-6614 - Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. 6615-6625 - Karlis Freivalds, Emils Ozolins, Agris Sostaks:
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time. 6626-6637 - Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun:
DetNAS: Backbone Search for Object Detection. 6638-6648 - Adil Salim, Dmitry Kovalev, Peter Richtárik:
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. 6649-6661 - Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim:
Fast AutoAugment. 6662-6672 - Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
On the Convergence Rate of Training Recurrent Neural Networks. 6673-6685 - Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo
Interval timing in deep reinforcement learning agents. 6686-6695 - Roi Livni, Yishay Mansour:
Graph-based Discriminators: Sample Complexity and Expressiveness. 6696-6705 - Stanislav Fort, Stanislaw Jastrzebski:
Large Scale Structure of Neural Network Loss Landscapes. 6706-6714 - Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene:
Learning Nonsymmetric Determinantal Point Processes. 6715-6725 - Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. 6726-6736 - Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni:
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. 6737-6746 - Seppo Virtanen, Mark A. Girolami:
Precision-Recall Balanced Topic Modelling. 6747-6756 - Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo:
Learning Sparse Distributions using Iterative Hard Thresholding. 6757-6766 - Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis:
Discriminative Topic Modeling with Logistic LDA. 6767-6777 - Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. 6778-6789 - Joan Serrà, Santiago Pascual, Carlos Segura:
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion. 6790-6800 - Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic:
Hyperparameter Learning via Distributional Transfer. 6801-6812 - Akinori Tanaka:
Discriminator optimal transport. 6813-6823 - David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus:
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. 6824-6834 - Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? 6835-6846 - Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun:
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations. 6847-6857 - Marco Cuturi, Olivier Teboul, Jean-Philippe Vert:
Differentiable Ranking and Sorting using Optimal Transport. 6858-6868 - Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. 6869-6879 - Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao:
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. 6880-6890 - Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye:
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem. 6891-6902 - Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko:
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. 6903-6913 - Boris Muzellec, Marco Cuturi:
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections. 6914-6925 - Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu:
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. 6926-6935 - Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos:
On the convergence of single-call stochastic extra-gradient methods. 6936-6946 - Shagun Ajmera, Shreya Rajagopal, Razi Rehman, Devarajan Sridharan:
Infra-slow brain dynamics as a marker for cognitive function and decline. 6947-6958 - Rui Zhang
Robust Principal Component Analysis with Adaptive Neighbors. 6959-6967 - Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila:
High-Quality Self-Supervised Deep Image Denoising. 6968-6978 - Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. 6979-6989 - Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou:
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs. 6990-7001 - Mark Herbster, James Robinson:
Online Prediction of Switching Graph Labelings with Cluster Specialists. 7002-7012 - Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Jieping Ye:
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response. 7013-7023 - Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. 7024-7035 - Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. 7036-7046 - Marek Petrik, Reazul Hasan Russel:
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. 7047-7056 - Alexis Conneau, Guillaume Lample:
Cross-lingual Language Model Pretraining. 7057-7067 - Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens:
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. 7068-7078 - Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. 7079-7089 - Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. 7090-7099 - Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo:
Are sample means in multi-armed bandits positively or negatively biased? 7100-7109 - Abi Komanduru, Jean Honorio:
On the Correctness and Sample Complexity of Inverse Reinforcement Learning. 7110-7119 - Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson:
VIREL: A Variational Inference Framework for Reinforcement Learning. 7120-7134 - Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe:
First Order Motion Model for Image Animation. 7135-7145 - Laurence Aitchison:
Tensor Monte Carlo: Particle Methods for the GPU era. 7146-7155 - Alban Laflaquière, Michaël Garcia Ortiz:
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction. 7156-7166 - Jiabin Liu, Bo Wang, Zhiquan Qi, Yingjie Tian, Yong Shi:
Learning from Label Proportions with Generative Adversarial Networks. 7167-7177 - Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff:
Efficient and Thrifty Voting by Any Means Necessary. 7178-7189 - Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu:
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. 7190-7201 - Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David D. Cox:
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. 7202-7213 - Erwan Lecarpentier, Emmanuel Rachelson:
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning. 7214-7223 - Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea:
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning. 7224-7234 - Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. 7235-7244 - Sebastian Mair, Ulf Brefeld:
Coresets for Archetypal Analysis. 7245-7253 - Adam Bielski, Paolo Favaro:
Emergence of Object Segmentation in Perturbed Generative Models. 7254-7264 - Xiyang Hu, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. 7265-7273 - Yue Sun, Nicolas Flammarion, Maryam Fazel:
Escaping from saddle points on Riemannian manifolds. 7274-7284 - Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer:
Multi-source Domain Adaptation for Semantic Segmentation. 7285-7298 - Carlo Ciliberto, Francis R. Bach, Alessandro Rudi:
Localized Structured Prediction. 7299-7309 - Sarath Yasodharan, Patrick Loiseau:
Nonzero-sum Adversarial Hypothesis Testing Games. 7310-7320 - David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. 7321-7332 - Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante
Modeling Tabular data using Conditional GAN. 7333-7343 - Lu Hou, Jinhua Zhu, James T. Kwok, Fei Gao, Tao Qin, Tie-Yan Liu:
Normalization Helps Training of Quantized LSTM. 7344-7354 - Clarice Poon, Jingwei Liang:
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration. 7355-7363 - Daniel E. Worrall, Max Welling:
Deep Scale-spaces: Equivariance Over Scale. 7364-7376 - Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau:
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series. 7377-7388 - Niloy Biswas, Pierre E. Jacob, Paul Vanetti:
Estimating Convergence of Markov chains with L-Lag Couplings. 7389-7399 - Piotr Indyk, Ali Vakilian
Learning-Based Low-Rank Approximations. 7400-7410 - Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo:
Implicit Regularization in Deep Matrix Factorization. 7411-7422 - Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari:
List-decodable Linear Regression. 7423-7432 - Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry:
Learning elementary structures for 3D shape generation and matching. 7433-7443 - Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell:
On the Hardness of Robust Classification. 7444-7453 - Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. 7454-7464 - Carl Allen, Ivana Balazevic, Timothy M. Hospedales:
What the Vec? Towards Probabilistically Grounded Embeddings. 7465-7475 - Marco Loog, Tom J. Viering, Alexander Mey
Minimizers of the Empirical Risk and Risk Monotonicity. 7476-7485 - Liangpeng Zhang, Ke Tang, Xin Yao:
Explicit Planning for Efficient Exploration in Reinforcement Learning. 7486-7495 - Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal:
Lower Bounds on Adversarial Robustness from Optimal Transport. 7496-7508 - Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios:
Neural Spline Flows. 7509-7520 - David Simchi-Levi, Yunzong Xu:
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints. 7521-7530 - Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder:
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization. 7531-7542 - Laura Rose Edmondson, Alejandro Jiménez-Rodríguez, Hannes P. Saal:
Nonlinear scaling of resource allocation in sensory bottlenecks. 7543-7552 - Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro:
Constrained Reinforcement Learning Has Zero Duality Gap. 7553-7563 - Niklas W. A. Gebauer, Michael Gastegger, Kristof Schütt:
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. 7564-7576 - Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran:
An adaptive nearest neighbor rule for classification. 7577-7586 - Lingxiao Huang, Shaofeng H.-C. Jiang, Nisheeth K. Vishnoi:
Coresets for Clustering with Fairness Constraints. 7587-7598 - David Novotný, Benjamin Graham, Jeremy Reizenstein:
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments. 7599-7610 - Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson:
MAVEN: Multi-Agent Variational Exploration. 7611-7622 - Florian Schäfer, Anima Anandkumar:
Competitive Gradient Descent. 7623-7633 - Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. 7634-7644 - Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell:
Continual Unsupervised Representation Learning. 7645-7655 - Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim:
Self-Routing Capsule Networks. 7656-7665 - Eduard Eiben, Robert Ganian, Iyad Kanj, Stefan Szeider:
The Parameterized Complexity of Cascading Portfolio Scheduling. 7666-7676 - Falcon Z. Dai, Matthew R. Walter:
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards. 7677-7685 - Rishidev Chaudhuri, Ila Fiete:
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes. 7686-7697 - Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov:
Sequence Modeling with Unconstrained Generation Order. 7698-7709 - Meng Qu, Jian Tang:
Probabilistic Logic Neural Networks for Reasoning. 7710-7720 - Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant:
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. 7721-7733 - Gecia Bravo Hermsdorff, Lee M. Gunderson:
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening. 7734-7745 - Xuechen Li, Yi Wu, Lester Mackey:
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. 7746-7758 - Qian Qian, Xiaoyuan Qian:
The Implicit Bias of AdaGrad on Separable Data. 7759-7767 - Guillaume Gautier, Rémi Bardenet, Michal Valko:
On two ways to use determinantal point processes for Monte Carlo integration. 7768-7777 - Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang, Larry S. Davis:
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition. 7778-7787 - Dennis Elbrächter, Julius Berner, Philipp Grohs:
How degenerate is the parametrization of neural networks with the ReLU activation function? 7788-7799 - Wenrui Zhang
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks. 7800-7811 - Guokun Lai, Zihang Dai, Yiming Yang, Shinjae Yoo:
Re-examination of the Role of Latent Variables in Sequence Modeling. 7812-7822 - Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa:
Max-value Entropy Search for Multi-Objective Bayesian Optimization. 7823-7833 - Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu:
Stein Variational Gradient Descent With Matrix-Valued Kernels. 7834-7844 - Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang:
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms. 7845-7855 - Roman Werpachowski, András György, Csaba Szepesvári:
Detecting Overfitting via Adversarial Examples. 7856-7866 - Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya:
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. 7867-7878 - Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel:
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. 7879-7889 - Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. 7890-7900 - Elliot Meyerson, Risto Miikkulainen:
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains. 7901-7912 - Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer G. Dy:
Solving Interpretable Kernel Dimensionality Reduction. 7913-7923 - Shuo Yang, Yanyao Shen, Sujay Sanghavi:
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. 7924-7934 - John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. 7935-7947 - Ron Banner, Yury Nahshan, Daniel Soudry:
Post training 4-bit quantization of convolutional networks for rapid-deployment. 7948-7956 - James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner:
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes. 7957-7968 - Ananda Theertha Suresh:
Differentially Private Anonymized Histograms. 7969-7979 - Sergül Aydöre, Tianhao Zhu, Dean P. Foster:
Dynamic Local Regret for Non-convex Online Forecasting. 7980-7989 - Emre Yolcu, Barnabás Póczos:
Learning Local Search Heuristics for Boolean Satisfiability. 7990-8001 - Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. 8002-8011 - Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. 8012-8023 - Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala:
PyTorch: An Imperative Style, High-Performance Deep Learning Library. 8024-8035 - Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Stability of Graph Scattering Transforms. 8036-8046 - Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu:
A Debiased MDI Feature Importance Measure for Random Forests. 8047-8057 - Simon S. Du, Yuping Luo, Ruosong Wang, Hanrui Zhang:
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle. 8058-8068 - Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis:
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. 8069-8079 - Guodong Zhang, James Martens, Roger B. Grosse:
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. 8080-8091 - Santosh S. Vempala, Andre Wibisono:
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices. 8092-8104 - Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi:
Learning Distributions Generated by One-Layer ReLU Networks. 8105-8115 - Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong, Ping Ma:
Large-scale optimal transport map estimation using projection pursuit. 8116-8127 - Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric:
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. 8128-8138 - Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li
On Exact Computation with an Infinitely Wide Neural Net. 8139-8148 - Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster:
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. 8149-8160 - Raymond A. Yeh, Yuan-Ting Hu, Alexander G. Schwing:
Chirality Nets for Human Pose Regression. 8161-8171 - Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao:
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. 8172-8182 - Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock:
Fast Decomposable Submodular Function Minimization using Constrained Total Variation. 8183-8193 - Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. 8194-8205 - Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance M. Kaplan, Jiawei Han:
Spherical Text Embedding. 8206-8215 - Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li:
Möbius Transformation for Fast Inner Product Search on Graph. 8216-8227 - Qi Liu, Maximilian Nickel, Douwe Kiela:
Hyperbolic Graph Neural Networks. 8228-8239 - Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth:
Average Individual Fairness: Algorithms, Generalization and Experiments. 8240-8249 - Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou:
Fixing the train-test resolution discrepancy. 8250-8260 - Lingge Li, Dustin S. Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi:
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes. 8261-8271 - Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael J. Wooldridge:
Manipulating a Learning Defender and Ways to Counteract. 8272-8281 - Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus:
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations. 8282-8292 - Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li:
Learning to Infer Implicit Surfaces without 3D Supervision. 8293-8304 - Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers. 8305-8316 - Aleksandar Bojchevski, Stephan Günnemann:
Certifiable Robustness to Graph Perturbations. 8317-8328 - Frederic Koehler:
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. 8329-8339 - Paul Gölz
Paradoxes in Fair Machine Learning. 8340-8350 - Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang:
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost. 8351-8363 - Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala
The spiked matrix model with generative priors. 8364-8375 - Francis Williams, Matthew Trager, Daniele Panozzo, Cláudio T. Silva, Denis Zorin, Joan Bruna:
Gradient Dynamics of Shallow Univariate ReLU Networks. 8376-8385 - Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani:
Robust and Communication-Efficient Collaborative Learning. 8386-8397 - Sauptik Dhar, Vladimir Cherkassky, Mohak Shah:
Multiclass Learning from Contradictions. 8398-8408 - Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury:
Learning from Trajectories via Subgoal Discovery. 8409-8419 - Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B. Wakin:
Distributed Low-rank Matrix Factorization With Exact Consensus. 8420-8430 - Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Köster, Ryan Reece, Sofia Samaniego de la Fuente, Vishal Subbiah, Michael James:
Online Normalization for Training Neural Networks. 8431-8441 - Arash Ardakani, Zhengyun Ji, Amir Ardakani, Warren J. Gross:
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic. 8442-8452 - Kimon Antonakopoulos, Elena Veronica Belmega, Panayotis Mertikopoulos:
An adaptive Mirror-Prox method for variational inequalities with singular operators. 8453-8463 - Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang:
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules. 8464-8476 - Bin Hu, Usman Ahmed Syed:
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory. 8477-8488 - Yunus Esencayi, Marco Gaboardi
Facility Location Problem in Differential Privacy Model Revisited. 8489-8498 - Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. 8499-8511 - Karl Krauth, Stephen Tu, Benjamin Recht:
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. 8512-8522 - Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
A Universally Optimal Multistage Accelerated Stochastic Gradient Method. 8523-8534 - Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli:
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. 8535-8545 - Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou:
Large Memory Layers with Product Keys. 8546-8557 - Gail Weiss, Yoav Goldberg, Eran Yahav:
Learning Deterministic Weighted Automata with Queries and Counterexamples. 8558-8569 - Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. 8570-8581 - Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. 8582-8591 - Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, Been Kim:
Visualizing and Measuring the Geometry of BERT. 8592-8600 - Jialin Wu, Raymond J. Mooney:
Self-Critical Reasoning for Robust Visual Question Answering. 8601-8611 - Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran:
Learning to Screen. 8612-8621 - Hao Yu:
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers. 8622-8631 - Gilad Baruch, Moran Baruch, Yoav Goldberg:
A Little Is Enough: Circumventing Defenses For Distributed Learning. 8632-8642 - Gunjan Verma, Ananthram Swami:
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks. 8643-8653 - Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni:
A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions. 8654-8664 - Shaofeng Zou, Tengyu Xu, Yingbin Liang:
Finite-Sample Analysis for SARSA with Linear Function Approximation. 8665-8675 - Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models. 8676-8686 - Colin Graber, Alexander G. Schwing:
Graph Structured Prediction Energy Networks. 8687-8698 - Alon Gonen, Elad Hazan, Shay Moran:
Private Learning Implies Online Learning: An Efficient Reduction. 8699-8709 - Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil A. Platanios, Sujith Ravi, Andrew Tomkins:
Graph Agreement Models for Semi-Supervised Learning. 8710-8720 - Ernesto Araya Valdivia, Yohann de Castro:
Latent distance estimation for random geometric graphs. 8721-8731 - Jennifer L. Cardona, Michael F. Howland, John O. Dabiri:
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network. 8732-8742 - Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling:
The Functional Neural Process. 8743-8754 - Robin Sandkühler, Simon Andermatt, Grzegorz Bauman, Sylvia Nyilas, Christoph Jud, Philippe C. Cattin:
Recurrent Registration Neural Networks for Deformable Image Registration. 8755-8765 - Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm:
Unsupervised State Representation Learning in Atari. 8766-8779 - Michael L. Wick, Swetasudha Panda, Jean-Baptiste Tristan:
Unlocking Fairness: a Trade-off Revisited. 8780-8789 - Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen:
Fisher Efficient Inference of Intractable Models. 8790-8800 - My Phan, Yasin Abbasi-Yadkori, Justin Domke:
Thompson Sampling and Approximate Inference. 8801-8811 - Yue Wang, Justin M. Solomon:
PRNet: Self-Supervised Learning for Partial-to-Partial Registration. 8812-8824 - Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making. 8825-8835 - Axel Brando, José A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio:
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians. 8836-8846 - Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White:
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. 8847-8857 - Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon:
Approximating the Permanent by Sampling from Adaptive Partitions. 8858-8869 - Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. 8870-8880 - Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon R. Graham:
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration. 8881-8891 - Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via the Differential Privacy Lens. 8892-8902 - Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu:
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points. 8903-8915 - Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten:
Parameter elimination in particle Gibbs sampling. 8916-8927 - Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su:
This Looks Like That: Deep Learning for Interpretable Image Recognition. 8928-8939 - Lun Huang, Wenmin Wang, Yaxian Xia, Jie Chen:
Adaptively Aligned Image Captioning via Adaptive Attention Time. 8940-8949 - Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull:
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. 8950-8961 - Adarsh Barik, Jean Honorio:
Learning Bayesian Networks with Low Rank Conditional Probability Tables. 8962-8971 - Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. 8972-8982 - Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth S. Spelke, Josh Tenenbaum, Tomer D. Ullman:
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. 8983-8993 - Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Chris Pal, Sungjin Ahn:
Neural Multisensory Scene Inference. 8994-9004 - Young Hun Jung, Ambuj Tewari:
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems. 9005-9014 - Zeyuan Allen-Zhu, Yuanzhi Li:
What Can ResNet Learn Efficiently, Going Beyond Kernels? 9015-9025 - Tamas Madarasz, Tim E. J. Behrens:
Better Transfer Learning with Inferred Successor Maps. 9026-9037 - Yifeng Fan, Tingran Gao, Zhizhen Zhao:
Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations. 9038-9050 - Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi:
Defending Against Neural Fake News. 9051-9062 - Michael Zhu:
Sample Adaptive MCMC. 9063-9074 - Junyu Zhang, Lin Xiao:
A Stochastic Composite Gradient Method with Incremental Variance Reduction. 9075-9085 - Ananya Uppal, Shashank Singh, Barnabás Póczos:
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses. 9086-9097 - Karim Ahmed, Lorenzo Torresani:
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing. 9098-9107 - Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Limitations of Lazy Training of Two-layers Neural Network. 9108-9118 - Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller:
Reconciling meta-learning and continual learning with online mixtures of tasks. 9119-9130 - Matthew Staib, Stefanie Jegelka:
Distributionally Robust Optimization and Generalization in Kernel Methods. 9131-9141 - Taco S. Cohen, Mario Geiger, Maurice Weiler:
A General Theory of Equivariant CNNs on Homogeneous Spaces. 9142-9153 - Mario Lezcano Casado:
Trivializations for Gradient-Based Optimization on Manifolds. 9154-9164 - Kevin Ellis, Maxwell I. Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama
Write, Execute, Assess: Program Synthesis with a REPL. 9165-9174 - Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt:
A Meta-Analysis of Overfitting in Machine Learning. 9175-9185 - Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng:
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs. 9186-9194 - Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin (Jerry) Zhu, Adish Singla
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models. 9195-9205 - Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi:
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback. 9206-9217 - Hao Cui, Roni Khardon:
Sampling Networks and Aggregate Simulation for Online POMDP Planning. 9218-9228 - Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm:
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks. 9229-9239 - Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec:
GNNExplainer: Generating Explanations for Graph Neural Networks. 9240-9251 - Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis:
Linear Stochastic Bandits Under Safety Constraints. 9252-9262 - Rohan Gala, Nathan W. Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül:
A coupled autoencoder approach for multi-modal analysis of cell types. 9263-9272 - Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim:
Towards Automatic Concept-based Explanations. 9273-9282 - Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt:
Deep Generative Video Compression. 9283-9294 - Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin:
Budgeted Reinforcement Learning in Continuous State Space. 9295-9305 - Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh:
Discovery of Useful Questions as Auxiliary Tasks. 9306-9317 - Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto:
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. 9318-9329 - Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna:
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. 9330-9340 - Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou:
Correlation clustering with local objectives. 9341-9350 - Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo:
Multiclass Performance Metric Elicitation. 9351-9360 - Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su:
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. 9361-9371 - Mikael Henaff:
Explicit Explore-Exploit Algorithms in Continuous State Spaces. 9372-9382 - Jinjin Tian, Aaditya Ramdas:
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. 9383-9391 - Vincent S. Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré:
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. 9392-9402 - James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi:
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. 9403-9413 - Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn:
Language as an Abstraction for Hierarchical Deep Reinforcement Learning. 9414-9426 - Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi:
Efficient online learning with kernels for adversarial large scale problems. 9427-9436 - Zhihui Zhu, Tianyu Ding, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal:
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning. 9437-9447 - Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz:
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. 9448-9458 - Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin:
Certified Adversarial Robustness with Additive Noise. 9459-9469 - Michela Meister, Tamás Sarlós, David P. Woodruff:
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels. 9470-9481 - Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar:
Non-Cooperative Inverse Reinforcement Learning. 9482-9493 - Rixon Crane, Fred Roosta:
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. 9494-9504 - Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cícero Nogueira dos Santos:
Sobolev Independence Criterion. 9505-9515 - Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller:
Maximum Entropy Monte-Carlo Planning. 9516-9524 - Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias:
Learning from brains how to regularize machines. 9525-9535 - Hunter Lang, Lin Xiao, Pengchuan Zhang:
Using Statistics to Automate Stochastic Optimization. 9536-9546 - Paul Micaelli, Amos J. Storkey:
Zero-shot Knowledge Transfer via Adversarial Belief Matching. 9547-9557 - Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter:
Differentiable Convex Optimization Layers. 9558-9570 - Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd T. Elliott:
Random Tessellation Forests. 9571-9581 - Blake Mason, Ardhendu Tripathy, Robert D. Nowak:
Learning Nearest Neighbor Graphs from Noisy Distance Samples. 9582-9592 - Michael R. Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton:
Lookahead Optimizer: k steps forward, 1 step back. 9593-9604 - Wenzheng Chen, Huan Ling, Jun Gao, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler:
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer. 9605-9616 - Nikolaos Ignatiadis, Stefan Wager:
Covariate-Powered Empirical Bayes Estimation. 9617-9629 - Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao:
Understanding the Role of Momentum in Stochastic Gradient Methods. 9630-9640 - Li Kevin Wenliang, Maneesh Sahani:
A neurally plausible model for online recognition and postdiction in a dynamical environment. 9641-9652 - Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn:
Guided Meta-Policy Search. 9653-9664 - Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. 9665-9675 - Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab S. Mirrokni, Jon Schneider:
Contextual Bandits with Cross-Learning. 9676-9685 - Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John F. Canny, Pieter Abbeel, Yun S. Song:
Evaluating Protein Transfer Learning with TAPE. 9686-9698 - Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao:
A Bayesian Theory of Conformity in Collective Decision Making. 9699-9708 - Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma:
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. 9709-9721 - Colin Wei, Tengyu Ma:
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. 9722-9733 - Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim:
A Benchmark for Interpretability Methods in Deep Neural Networks. 9734-9745 - Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory Efficient Adaptive Optimization. 9746-9755 - Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni:
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions. 9756-9766 - Miguel Vaquero, Jorge Cortés:
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control. 9767-9776 - Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. 9777-9787 - Brenden M. Lake:
Compositional generalization through meta sequence-to-sequence learning. 9788-9798 - Lingrui Gan, Xinming Yang, Naveen N. Narisetty, Feng Liang:
Bayesian Joint Estimation of Multiple Graphical Models. 9799-9809 - Jian Wu, Peter I. Frazier:
Practical Two-Step Lookahead Bayesian Optimization. 9810-9820 - Yunfei Teng, Wenbo Gao, François Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller:
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models. 9821-9831 - Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang:
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks. 9832-9842 - Junteng Jia, Austin R. Benson:
Neural Jump Stochastic Differential Equations. 9843-9854 - Qi Zhao, Yusu Wang:
Learning metrics for persistence-based summaries and applications for graph classification. 9855-9866 - Steve Hanneke, Samory Kpotufe:
On the Value of Target Data in Transfer Learning. 9867-9877 - Adithya M. Devraj, Jianshu Chen:
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization. 9878-9888 - Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song:
On Robustness of Principal Component Regression. 9889-9900 - Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha:
Meta Learning with Relational Information for Short Sequences. 9901-9912 - Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen:
Residual Flows for Invertible Generative Modeling. 9913-9923 - Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
Multi-Agent Common Knowledge Reinforcement Learning. 9924-9935 - Antreas Antoniou, Amos J. Storkey:
Learning to Learn By Self-Critique. 9936-9946 - Greg Yang:
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes. 9947-9960 - Tian Qi Chen, David Duvenaud:
Neural Networks with Cheap Differential Operators. 9961-9971 - Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao:
Transductive Zero-Shot Learning with Visual Structure Constraint. 9972-9982 - Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta:
Dying Experts: Efficient Algorithms with Optimal Regret Bounds. 9983-9992 - Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht:
Model Similarity Mitigates Test Set Overuse. 9993-10002 - Ben Sorscher, Gabriel Mel, Surya Ganguli, Samuel A. Ocko:
A unified theory for the origin of grid cells through the lens of pattern formation. 10003-10013 - Wenbo Ren, Jia Liu, Ness B. Shroff:
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons. 10014-10024 - Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis:
Hierarchical Decision Making by Generating and Following Natural Language Instructions. 10025-10034 - Qian Lou, Lei Jiang:
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data. 10035-10043 - Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni:
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond. 10044-10054 - Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
A Game Theoretic Approach to Class-wise Selective Rationalization. 10055-10065 - Emmanouil V. Vlatakis-Gkaragkounis
Efficiently avoiding saddle points with zero order methods: No gradients required. 10066-10077 - Jenelle Feather, Alex Durango, Ray Gonzalez, Josh H. McDermott:
Metamers of neural networks reveal divergence from human perceptual systems. 10078-10089 - Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li:
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization. 10090-10100 - Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg:
Decentralized sketching of low rank matrices. 10101-10110 - Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 퓁1-Norm Loss. 10111-10121 - Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey:
Efficient Forward Architecture Search. 10122-10131 - Siavash Khodadadeh, Ladislau Bölöni, Mubarak Shah:
Unsupervised Meta-Learning for Few-Shot Image Classification. 10132-10142 - Zhibing Zhao, Lirong Xia:
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders. 10143-10153 - Horia Mania, Stephen Tu, Benjamin Recht:
Certainty Equivalence is Efficient for Linear Quadratic Control. 10154-10164 - Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski:
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. 10165-10174 - Naman Agarwal, Elad Hazan, Karan Singh:
Logarithmic Regret for Online Control. 10175-10184 - Matthew Reimherr, Jordan Awan:
Elliptical Perturbations for Differential Privacy. 10185-10196 - Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, Yang Liu:
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. 10197-10207 - Matthew Reimherr, Jordan Awan:
KNG: The K-Norm Gradient Mechanism. 10208-10219 - Patrick Schwab, Walter Karlen:
CXPlain: Causal Explanations for Model Interpretation under Uncertainty. 10220-10230 - Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang:
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning. 10231-10241 - Corey Snyder, Minh Do:
STREETS: A Novel Camera Network Dataset for Traffic Flow. 10242-10253 - Gautam Singh, Jaesik Yoon, Youngsung Son, Sungjin Ahn:
Sequential Neural Processes. 10254-10264 - Hao Sun, Zhizhong Li, Xiaotong Liu, Bolei Zhou, Dahua Lin:
Policy Continuation with Hindsight Inverse Dynamics. 10265-10275 - Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
Learning to Self-Train for Semi-Supervised Few-Shot Classification. 10276-10286 - Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. 10287-10298 - Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani:
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. 10299-10309 - Joe Kileel, Matthew Trager, Joan Bruna:
On the Expressive Power of Deep Polynomial Neural Networks. 10310-10319 - Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. 10320-10330 - Zeyuan Allen-Zhu, Yuanzhi Li:
Can SGD Learn Recurrent Neural Networks with Provable Generalization? 10331-10341 - Raef Bassily, Shay Moran, Noga Alon:
Limits of Private Learning with Access to Public Data. 10342-10352 - Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams:
Discrete Object Generation with Reversible Inductive Construction. 10353-10363 - Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama:
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models. 10364-10375 - Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher:
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. 10376-10386 - Larkin Flodin, Venkata Gandikota, Arya Mazumdar:
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing. 10387-10396 - Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri:
Bandits with Feedback Graphs and Switching Costs. 10397-10407 - Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. 10408-10418 - Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Statistical-Computational Tradeoff in Single Index Models. 10419-10426 - Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy V. Bobashev, Lawrence Carin:
On Fenchel Mini-Max Learning. 10427-10439 - Jinhao Dong, Tong Lin:
MarginGAN: Adversarial Training in Semi-Supervised Learning. 10440-10449 - Emmanouil V. Vlatakis-Gkaragkounis
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games. 10450-10461 - Guanghui Lan, Zhize Li, Yi Zhou:
A unified variance-reduced accelerated gradient method for convex optimization. 10462-10472 - Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. 10473-10484 - Wasim Huleihel, Arya Mazumdar, Muriel Médard, Soumyabrata Pal:
Same-Cluster Querying for Overlapping Clusters. 10485-10495 - Raman Arora, Teodor Vanislavov Marinov:
Efficient Convex Relaxations for Streaming PCA. 10496-10505 - Haohan Wang, Songwei Ge, Zachary C. Lipton, Eric P. Xing:
Learning Robust Global Representations by Penalizing Local Predictive Power. 10506-10518 - Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn:
Unsupervised Curricula for Visual Meta-Reinforcement Learning. 10519-10530 - Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Sample Complexity of Learning Mixture of Sparse Linear Regressions. 10531-10540 - Jeff Donahue, Karen Simonyan:
Large Scale Adversarial Representation Learning. 10541-10551 - Jiaxuan You, Haoze Wu, Clark W. Barrett, Raghuram Ramanujan, Jure Leskovec:
G2SAT: Learning to Generate SAT Formulas. 10552-10563 - Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy. 10564-10575 - Yair Bartal, Nova Fandina, Ofer Neiman:
Dimensionality reduction: theoretical perspective on practical measures. 10576-10588 - Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback. 10589-10598 - Multilabel reductions: what is my loss optimising? 10599-10610
- Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. 10611-10621 - Ziyin Liu, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda:
Deep Gamblers: Learning to Abstain with Portfolio Theory. 10622-10632 - Tengyu Xu, Shaofeng Zou, Yingbin Liang:
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples. 10633-10643 - Boyu Wang, Jorge A. Mendez, Mingbo Cai, Eric Eaton:
Transfer Learning via Minimizing the Performance Gap Between Domains. 10644-10654 - Lemeng Wu, Dilin Wang, Qiang Liu:
Splitting Steepest Descent for Growing Neural Architectures. 10655-10665 - Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. 10666-10676 - Aditya Golatkar, Alessandro Achille, Stefano Soatto:
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence. 10677-10687 - Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. 10688-10699 - Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna R. Narayanan, Nick Duffield, Mingyuan Zhou
Variational Graph Recurrent Neural Networks. 10700-10710 - Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna R. Narayanan, Nick Duffield, Mingyuan Zhou
Semi-Implicit Graph Variational Auto-Encoders. 10711-10722 - Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih:
Unsupervised Learning of Object Keypoints for Perception and Control. 10723-10733 - Xueying Bai, Jian Guan, Hongning Wang:
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation. 10734-10745 - Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta:
Optimizing Generalized Rate Metrics with Three Players. 10746-10757 - Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak:
Consistency-based Semi-supervised Learning for Object detection. 10758-10767 - Xingye Qiao, Jiexin Duan, Guang Cheng:
Rates of Convergence for Large-scale Nearest Neighbor Classification. 10768-10779 - Jessica Finocchiaro, Rafael M. Frongillo, Bo Waggoner:
An Embedding Framework for Consistent Polyhedral Surrogates. 10780-10790 - Chao Li, Shangqian Gao, Cheng Deng, De Xie, Wei Liu:
Cross-Modal Learning with Adversarial Samples. 10791-10801 - Jun Yang, Shengyang Sun, Daniel M. Roy:
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. 10802-10812 - Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi:
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. 10813-10823 - Eui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov:
Program Synthesis and Semantic Parsing with Learned Code Idioms. 10824-10834 - Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. 10835-10845 - Jonathan Lacotte, Mert Pilanci, Marco Pavone:
High-Dimensional Optimization in Adaptive Random Subspaces. 10846-10856 - Xiaoyun Li, Ping Li:
Random Projections with Asymmetric Quantization. 10857-10866 - Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen:
Superposition of many models into one. 10867-10876 - Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld:
Private Testing of Distributions via Sample Permutations. 10877-10888 - Rui Ray Zhang, Xingwu Liu, Yuyi Wang, Liwei Wang:
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds. 10889-10899 - Devansh Arpit, Víctor Campos, Yoshua Bengio:
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets. 10900-10909 - Andrew Cotter, Maya R. Gupta, Harikrishna Narasimhan:
On Making Stochastic Classifiers Deterministic. 10910-10920 - Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo:
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection. 10921-10931 - Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Improving Black-box Adversarial Attacks with a Transfer-based Prior. 10932-10942 - Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. 10943-10953 - Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang:
Statistical Model Aggregation via Parameter Matching. 10954-10964 - Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
On the (In)fidelity and Sensitivity of Explanations. 10965-10976 - Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. 10977-10988 - Yi Hao, Alon Orlitsky:
The Broad Optimality of Profile Maximum Likelihood. 10989-11001 - Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. 11002-11012 - Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy:
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. 11013-11023 - Aditya Bhaskara, Maheshakya Wijewardena:
On Distributed Averaging for Stochastic k-PCA. 11024-11033 - Ke Wang, Hang Hua, Xiaojun Wan:
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation. 11034-11044 - Sumeet Katariya, Ardhendu Tripathy, Robert D. Nowak:
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking. 11045-11055 - Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. 11056-11068 - Dheeraj Baby, Yu-Xiang Wang:
Online Forecasting of Total-Variation-bounded Sequences. 11069-11079 - Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe:
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. 11080-11092 - Shreyas Saxena, Oncel Tuzel, Dennis DeCoste:
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum. 11093-11103 - Yi Hao, Alon Orlitsky:
Unified Sample-Optimal Property Estimation in Near-Linear Time. 11104-11114 - Shuai Zhao
Region Mutual Information Loss for Semantic Segmentation. 11115-11125 - J. Zico Kolter, Gaurav Manek:
Learning Stable Deep Dynamics Models. 11126-11134 - Simao Herdade, Armin Kappeler, Kofi Boakye, Joao Soares:
Image Captioning: Transforming Objects into Words. 11135-11145 - Aditya Bhaskara, Sharvaree Vadgama, Hong Xu:
Greedy Sampling for Approximate Clustering in the Presence of Outliers. 11146-11155 - Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua M. Susskind:
Adversarial Fisher Vectors for Unsupervised Representation Learning. 11156-11166 - Pasha Khosravi, YooJung Choi
On Tractable Computation of Expected Predictions. 11167-11178 - Jiatao Gu, Changhan Wang, Junbo Zhao:
Levenshtein Transformer. 11179-11189 - Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang:
Unlabeled Data Improves Adversarial Robustness. 11190-11201 - Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski:
Machine Teaching of Active Sequential Learners. 11202-11213 - Takumi Kobayashi:
Gaussian-Based Pooling for Convolutional Neural Networks. 11214-11224 - Albert E. Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai:
Meta Architecture Search. 11225-11235 - Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue:
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. 11236-11246 - Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu:
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. 11247-11256 - Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao:
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test. 11257-11268 - Anmol Kagrecha, Jayakrishnan Nair, Krishna P. Jagannathan:
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards. 11269-11278 - Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. 11279-11288 - Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, Greg Yang:
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. 11289-11300 - Ahmed M. Alaa, Mihaela van der Schaar:
Demystifying Black-box Models with Symbolic Metamodels. 11301-11311 - Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang:
Neural Temporal-Difference Learning Converges to Global Optima. 11312-11322 - Baoxiang Wang, Nidhi Hegde:
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces. 11323-11333 - Ahmed M. Alaa, Mihaela van der Schaar:
Attentive State-Space Modeling of Disease Progression. 11334-11344 - Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet:
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback. 11345-11354 - Arun Jambulapati, Aaron Sidford, Kevin Tian:
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. 11355-11366 - Julaiti Alafate, Yoav Freund:
Faster Boosting with Smaller Memory. 11367-11376 - Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Variance Reduction for Matrix Games. 11377-11388 - Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon:
Learning Neural Networks with Adaptive Regularization. 11389-11400 - Michal Derezinski, Michael W. Mahoney:
Distributed estimation of the inverse Hessian by determinantal averaging. 11401-11411 - Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli:
Smoothing Structured Decomposable Circuits. 11412-11422 - Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George J. Pappas:
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks. 11423-11434 - Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon:
Provable Non-linear Inductive Matrix Completion. 11435-11445 - Shuai Zheng, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. 11446-11456 - Trevor Campbell, Boyan Beronov
Sparse Variational Inference: Bayesian Coresets from Scratch. 11457-11468 - Nima Hamidi, Mohsen Bayati, Kapil Gupta:
Personalizing Many Decisions with High-Dimensional Covariates. 11469-11480 - Moshe Shenfeld, Katrina Ligett:
A Necessary and Sufficient Stability Notion for Adaptive Generalization. 11481-11490 - Daniel Levy, John C. Duchi:
Necessary and Sufficient Geometries for Gradient Methods. 11491-11501 - Nikhil Ghosh, Yuxin Chen, Yisong Yue:
Landmark Ordinal Embedding. 11502-11511 - Amin Jaber, Jiji Zhang, Elias Bareinboim:
Identification of Conditional Causal Effects under Markov Equivalence. 11512-11520 - Vaden Masrani, Tuan Anh Le, Frank Wood:
The Thermodynamic Variational Objective. 11521-11530 - Paul Hand, Babhru Joshi:
Global Guarantees for Blind Demodulation with Generative Priors. 11531-11541 - Michal Derezinski, Daniele Calandriello, Michal Valko:
Exact sampling of determinantal point processes with sublinear time preprocessing. 11542-11554 - Joshua Tobin, Wojciech Zaremba, Pieter Abbeel:
Geometry-Aware Neural Rendering. 11555-11565 - Taesup Kim, Sungjin Ahn, Yoshua Bengio:
Variational Temporal Abstraction. 11566-11575 - Amir Abboud, Vincent Cohen-Addad, Hussein Houdrouge:
Subquadratic High-Dimensional Hierarchical Clustering. 11576-11586 - Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern:
Learning Auctions with Robust Incentive Guarantees. 11587-11597 - Kaiqing Zhang, Zhuoran Yang, Tamer Basar:
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games. 11598-11610 - Vaishnavh Nagarajan, J. Zico Kolter:
Uniform convergence may be unable to explain generalization in deep learning. 11611-11622 - Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid:
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. 11623-11635 - Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran:
DTWNet: a Dynamic Time Warping Network. 11636-11646 - Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Structured Graph Learning Via Laplacian Spectral Constraints. 11647-11658 - Chao Tao, Saúl A. Blanco, Jian Peng, Yuan Zhou:
Thresholding Bandit with Optimal Aggregate Regret. 11659-11668 - Yuanzhi Li, Colin Wei, Tengyu Ma:
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. 11669-11680 - Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas:
Rethinking Kernel Methods for Node Representation Learning on Graphs. 11681-11692 - Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. 11693-11704 - Pan Li, I (Eli) Chien, Olgica Milenkovic:
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection. 11705-11716 - Amanda Gentzel, Dan Garant, David D. Jensen:
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. 11717-11727 - Zheng Li, Christopher De Sa:
Dimension-Free Bounds for Low-Precision Training. 11728-11738 - Sanjay P. Bhat, Prashanth L. A.:
Concentration of risk measures: A Wasserstein distance approach. 11739-11748 - Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon:
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. 11749-11760 - Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. 11761-11771 - Huong Ha, Santu Rana, Sunil Gupta, Thanh Tang Nguyen, Hung Tran-The, Svetha Venkatesh:
Bayesian Optimization with Unknown Search Space. 11772-11781 - Avner May, Jian Zhang, Tri Dao, Christopher Ré:
On the Downstream Performance of Compressed Word Embeddings. 11782-11793 - Jose H. Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou:
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss. 11794-11803 - Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Neural Relational Inference with Fast Modular Meta-learning. 11804-11815 - Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio:
Gradient based sample selection for online continual learning. 11816-11825 - Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami:
Attribution-Based Confidence Metric For Deep Neural Networks. 11826-11837 - Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Theoretical evidence for adversarial robustness through randomization. 11838-11848 - Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia:
Online Continual Learning with Maximal Interfered Retrieval. 11849-11860 - Rahul Gupta, Aditya Kanade, Shirish K. Shevade:
Neural Attribution for Semantic Bug-Localization in Student Programs. 11861-11871 - Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu:
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. 11872-11882 - Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy Liang:
SPoC: Search-based Pseudocode to Code. 11883-11894 - Yang Song, Stefano Ermon:
Generative Modeling by Estimating Gradients of the Data Distribution. 11895-11907 - Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze:
Adversarial Music: Real world Audio Adversary against Wake-word Detection System. 11908-11918 - Muhammad Osama, Dave Zachariah, Peter Stoica:
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees. 11919-11928 - Kolyan Ray, Botond Szabó:
Debiased Bayesian inference for average treatment effects. 11929-11939 - Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson:
Margin-Based Generalization Lower Bounds for Boosted Classifiers. 11940-11949 - Julian Zimmert, Tor Lattimore:
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. 11950-11959 - Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim:
Graph Transformer Networks. 11960-11970 - Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou:
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder. 11971-11981 - Fariborz Salehi, Ehsan Abbasi, Babak Hassibi:
The Impact of Regularization on High-dimensional Logistic Regression. 11982-11992 - Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek:
Adaptive Density Estimation for Generative Models. 11993-12003 - Fabian Latorre, Armin Eftekhari, Volkan Cevher
Fast and Provable ADMM for Learning with Generative Priors. 12004-12016 - Yoan Russac, Claire Vernade, Olivier Cappé:
Weighted Linear Bandits for Non-Stationary Environments. 12017-12026 - Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Improved Regret Bounds for Bandit Combinatorial Optimization. 12027-12036 - Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong:
Pareto Multi-Task Learning. 12037-12047 - Etienne Boursier, Vianney Perchet:
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits. 12048-12057 - Vighnesh Leonardo Shiv, Chris Quirk:
Novel positional encodings to enable tree-based transformers. 12058-12068 - Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. 12069-12079 - Shinji Ito:
Submodular Function Minimization with Noisy Evaluation Oracle. 12080-12090 - Radu Marinescu, Rina Dechter:
Counting the Optimal Solutions in Graphical Models. 12091-12101 - Shuyue Hu, Chin-wing Leung, Ho-fung Leung:
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach. 12102-12112 - Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao:
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling. 12113-12122 - Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng:
Bootstrapping Upper Confidence Bound. 12123-12133 - Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling:
Integer Discrete Flows and Lossless Compression. 12134-12144 - Mathieu Blondel:
Structured Prediction with Projection Oracles. 12145-12156 - Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla:
A Primal Dual Formulation For Deep Learning With Constraints. 12157-12168 - Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
Screening Sinkhorn Algorithm for Regularized Optimal Transport. 12169-12179 - Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj:
PAC-Bayes Un-Expected Bernstein Inequality. 12180-12191 - Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, Pushmeet Kohli:
Are Labels Required for Improving Adversarial Robustness? 12192-12202 - Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor:
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. 12203-12213 - Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh:
Multi-objective Bayesian optimisation with preferences over objectives. 12214-12224 - Pooria Joulani, András György, Csaba Szepesvári:
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging. 12225-12235 - David Widmann, Fredrik Lindsten, Dave Zachariah:
Calibration tests in multi-class classification: A unifying framework. 12236-12246 - Suman V. Ravuri, Oriol Vinyals:
Classification Accuracy Score for Conditional Generative Models. 12247-12258 - Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao:
Theoretical Analysis of Adversarial Learning: A Minimax Approach. 12259-12269 - Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos:
Multiagent Evaluation under Incomplete Information. 12270-12282 - Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi:
Tree-Sliced Variants of Wasserstein Distances. 12283-12294 - Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. 12295-12305 - Meyer Scetbon, Gaël Varoquaux:
Comparing distributions: 퓁1 geometry improves kernel two-sample testing. 12306-12316 - Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh:
Robustness Verification of Tree-based Models. 12317-12328 - Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. 12329-12338 - Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Fast and Accurate Stochastic Gradient Estimation. 12339-12349 - Igor Colin, Ludovic Dos Santos, Kevin Scaman:
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. 12350-12359 - Biao Zhang, Rico Sennrich:
Root Mean Square Layer Normalization. 12360-12371 - Ehsan Abbasi, Fariborz Salehi, Babak Hassibi:
Universality in Learning from Linear Measurements. 12372-12382 - Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko:
Planning in entropy-regularized Markov decision processes and games. 12383-12392 - Cheng Tang:
Exponentially convergent stochastic k-PCA without variance reduction. 12393-12404 - Jérôme Revaud, César Roberto de Souza, Martin Humenberger, Philippe Weinzaepfel:
R2D2: Reliable and Repeatable Detector and Descriptor. 12405-12415 - Shin Matsushima, Maria Brbic:
Selective Sampling-based Scalable Sparse Subspace Clustering. 12416-12425 - Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
A General Framework for Symmetric Property Estimation. 12426-12436 - Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps:
Structured Variational Inference in Continuous Cox Process Models. 12437-12447 - Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charlie Deck, Joel Z. Leibo, Charles Blundell:
Generalization of Reinforcement Learners with Working and Episodic Memory. 12448-12457 - Meera Pai, Animesh Kumar:
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor. 12458-12466 - Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos:
Hindsight Credit Assignment. 12467-12476 - Daniel Kumor, Bryant Chen, Elias Bareinboim:
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. 12477-12486 - Ning Miao, Hao Zhou, Chengqi Zhao, Wenxian Shi, Lei Li:
Kernelized Bayesian Softmax for Text Generation. 12487-12497 - Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine:
When to Trust Your Model: Model-Based Policy Optimization. 12498-12509 - Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale:
Correlation Clustering with Adaptive Similarity Queries. 12510-12519 - Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius:
Control What You Can: Intrinsically Motivated Task-Planning Agent. 12520-12531 - Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Selecting causal brain features with a single conditional independence test per feature. 12532-12543 - Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. 12544-12555 - Andrei Kulunchakov, Julien Mairal:
A Generic Acceleration Framework for Stochastic Composite Optimization. 12556-12567 - Nicole Mücke, Gergely Neu, Lorenzo Rosasco:
Beating SGD Saturation with Tail-Averaging and Minibatching. 12568-12577 - Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu:
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. 12578-12588 - Antonio Orvieto, Aurélien Lucchi:
Continuous-time Models for Stochastic Optimization Algorithms. 12589-12601 - Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang:
Curriculum-guided Hindsight Experience Replay. 12602-12613 - Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, Cheng Wu:
Implicit Semantic Data Augmentation for Deep Networks. 12614-12623 - Yann N. Dauphin, Samuel S. Schoenholz:
MetaInit: Initializing learning by learning to initialize. 12624-12636 - Xuhui Fan, Bin Li, Caoyuan Li, Scott A. Sisson, Ling Chen
Scalable Deep Generative Relational Model with High-Order Node Dependence. 12637-12647 - Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan
Random Path Selection for Continual Learning. 12648-12658 - Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. 12659-12670 - Antonio Orvieto, Aurélien Lucchi:
Shadowing Properties of Optimization Algorithms. 12671-12682 - Dominik Janzing:
Causal Regularization. 12683-12693 - Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran:
Learning Hawkes Processes from a handful of events. 12694-12704 - Mickaël Chen, Thierry Artières, Ludovic Denoyer:
Unsupervised Object Segmentation by Redrawing. 12705-12716 - Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard:
Regret Bounds for Learning State Representations in Reinforcement Learning. 12717-12727 - Felipe A. Tobar:
Band-Limited Gaussian Processes: The Sinc Kernel. 12728-12738 - Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. 12739-12750 - Valerio Perrone, Huibin Shen:
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. 12751-12761 - Edoardo Manino
Streaming Bayesian Inference for Crowdsourced Classification. 12762-12772 - Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. 12773-12784 - Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo:
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 12785-12796 - Yair Marom, Dan Feldman:
k-Means Clustering of Lines for Big Data. 12797-12806 - Stefan Meintrup, Alexander Munteanu, Dennis Rohde:
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves. 12807-12817 - Andrei Liviu Nicolicioiu, Iulia Duta, Marius Leordeanu:
Recurrent Space-time Graph Neural Networks. 12818-12830 - Bertrand Charpentier, Marin Bilos, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. 12831-12840 - Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge:
Accurate, reliable and fast robustness evaluation. 12841-12851 - David Gamarnik, Julia Gaudio:
Sparse High-Dimensional Isotonic Regression. 12852-12862 - Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang:
Triad Constraints for Learning Causal Structure of Latent Variables. 12863-12872 - Alberto Bietti, Julien Mairal:
On the Inductive Bias of Neural Tangent Kernels. 12873-12884 - Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan
Cross-Domain Transferability of Adversarial Perturbations. 12885-12895 - Don Kurian Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain:
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. 12896-12906 - Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel quadrature with DPPs. 12907-12917 - Yiwei Liu, Jiamou Liu, Zijian Zhang, Liehuang Zhu, Angsheng Li:
REM: From Structural Entropy to Community Structure Deception. 12918-12928 - Carl Doersch, Andrew Zisserman:
Sim2real transfer learning for 3D human pose estimation: motion to the rescue. 12929-12941 - Jonathan Sauder, Bjarne Sievers:
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space. 12942-12952 - Tristan Milne:
Piecewise Strong Convexity of Neural Networks. 12953-12963 - Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. 12964-12976 - James P. Bailey, Georgios Piliouras:
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes. 12977-12987 - Amit Daniely, Elad Granot:
Generalization Bounds for Neural Networks via Approximate Description Length. 12988-12996 - Maksym Andriushchenko, Matthias Hein:
Provably robust boosted decision stumps and trees against adversarial attacks. 12997-13008 - Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Neural Networks. 13009-13020 - Kishor Jothimurugan, Rajeev Alur, Osbert Bastani:
A Composable Specification Language for Reinforcement Learning Tasks. 13021-13030 - André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. 13031-13041 - Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon:
Unified Language Model Pre-training for Natural Language Understanding and Generation. 13042-13054 - Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti:
Learning to Correlate in Multi-Player General-Sum Sequential Games. 13055-13065 - Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen:
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match. 13066-13076 - Justin Cosentino, Jun Zhu:
Generative Well-intentioned Networks. 13077-13088 - Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil:
Online-Within-Online Meta-Learning. 13089-13099 - Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. 13100-13110 - Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel:
Biases for Emergent Communication in Multi-agent Reinforcement Learning. 13111-13121 - Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama:
Episodic Memory in Lifelong Language Learning. 13122-13131 - Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. 13132-13143 - Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora:
Communication-efficient Distributed SGD with Sketching. 13144-13154 - Rodolfo Corona, Stephan Alaniz, Zeynep Akata:
Modeling Conceptual Understanding in Image Reference Games. 13155-13165 - Maria Jahja, David C. Farrow, Roni Rosenfeld
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. 13166-13175 - Sariel Har-Peled
Near Neighbor: Who is the Fairest of Them All? 13176-13187 - Arnak S. Dalalyan, Philip Thompson:
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator. 13188-13198 - Guannan Zhang, Jiaxin Zhang, Jacob D. Hinkle:
Learning nonlinear level sets for dimensionality reduction in function approximation. 13199-13208 - Yi Chern Tan, L. Elisa Celis:
Assessing Social and Intersectional Biases in Contextualized Word Representations. 13209-13220 - Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal:
Online Convex Matrix Factorization with Representative Regions. 13221-13231 - Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, Ngai-Man Cheung:
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game. 13232-13243 - Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava:
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. 13244-13254 - Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer:
A Fourier Perspective on Model Robustness in Computer Vision. 13255-13265 - Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. 13266-13276 - Borja Balle, Gilles Barthe, Marco Gaboardi
Privacy Amplification by Mixing and Diffusion Mechanisms. 13277-13287 - Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab S. Mirrokni:
Variance Reduction in Bipartite Experiments through Correlation Clustering. 13288-13298 - Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat:
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. 13299-13309 - Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler:
Metalearned Neural Memory. 13310-13321 - Mohammad Sadegh Talebi, Odalric-Ambrym Maillard:
Learning Multiple Markov Chains via Adaptive Allocation. 13322-13332 - Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. 13333-13345 - Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham:
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. 13346-13356 - Yixin Wang, David M. Blei:
Variational Bayes under Model Misspecification. 13357-13367 - Lei Wu, Qingcan Wang, Chao Ma:
Global Convergence of Gradient Descent for Deep Linear Residual Networks. 13368-13377 - Raman Arora, Jalaj Upadhyay:
On Differentially Private Graph Sparsification and Applications. 13378-13389 - He Lyu, Ningyu Sha, Shuyang Qin, Ming Yan, Yuying Xie, Rongrong Wang:
Manifold denoising by Nonlinear Robust Principal Component Analysis. 13390-13400 - Junzhe Zhang, Elias Bareinboim:
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. 13401-13411 - Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. 13412-13421 - Se-Young Yun, Alexandre Proutière:
Optimal Sampling and Clustering in the Stochastic Block Model. 13422-13430 - Dexiong Chen, Laurent Jacob, Julien Mairal:
Recurrent Kernel Networks. 13431-13442 - Chhavi Yadav, Léon Bottou:
Cold Case: The Lost MNIST Digits. 13443-13452 - John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell:
Hierarchical Optimal Transport for Multimodal Distribution Alignment. 13453-13463 - Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng:
Exploration via Hindsight Goal Generation. 13464-13474 - Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aäron van den Oord:
Shaping Belief States with Generative Environment Models for RL. 13475-13487 - Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel:
Globally Optimal Learning for Structured Elliptical Losses. 13488-13497 - Enrique Sanchez, Georgios Tzimiropoulos:
Object landmark discovery through unsupervised adaptation. 13498-13509 - Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour:
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. 13510-13521 - Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum:
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks. 13522-13532 - Ashia C. Wilson, Lester Mackey, Andre Wibisono:
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. 13533-13543 - Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
RUDDER: Return Decomposition for Delayed Rewards. 13544-13555 - Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky:
Graph Normalizing Flows. 13556-13566 - Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel:
Explanations can be manipulated and geometry is to blame. 13567-13578 - Aymeric Dieuleveut, Kumar Kshitij Patel:
Communication trade-offs for Local-SGD with large step size. 13579-13590 - Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie:
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics. 13591-13601 - Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause:
No-Regret Learning in Unknown Games with Correlated Payoffs. 13602-13611 - Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon, Mikhail Yurochkin:
Alleviating Label Switching with Optimal Transport. 13612-13622 - Yao Fu, Yansong Feng, John P. Cunningham:
Paraphrase Generation with Latent Bag of Words. 13623-13634 - Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. 13635-13646 - Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen:
Compacting, Picking and Growing for Unforgetting Continual Learning. 13647-13657 - Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Àgata Lapedriza, Rosalind W. Picard:
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems. 13658-13669 - Andrew Stirn, Tony Jebara, David A. Knowles:
A New Distribution on the Simplex with Auto-Encoding Applications. 13670-13680 - Xia Xiao, Zigeng Wang, Sanguthevar Rajasekaran:
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters. 13681-13691 - Eszter Vértes, Maneesh Sahani:
A neurally plausible model learns successor representations in partially observable environments. 13692-13702 - Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz:
Learning about an exponential amount of conditional distributions. 13703-13714 - Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon, Darshan Patil, Nghia Le, Daniel Ferreira:
Towards modular and programmable architecture search. 13715-13725 - Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck:
Towards Hardware-Aware Tractable Learning of Probabilistic Models. 13726-13736 - Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan:
On Robustness to Adversarial Examples and Polynomial Optimization. 13737-13747 - Suhas Jayaram Subramanya, Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnaswamy, Rohan Kadekodi:
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node. 13748-13758 - Chuang Wang, Hong Hu, Yue M. Lu:
A Solvable High-Dimensional Model of GAN. 13759-13768 - Victor Veitch, Yixin Wang, David M. Blei:
Using Embeddings to Correct for Unobserved Confounding in Networks. 13769-13779 - Igor Kuralenok, Vasilii Ershov, Igor Labutin:
MonoForest framework for tree ensemble analysis. 13780-13789 - Sayak Ray Chowdhury, Aditya Gopalan:
Bayesian Optimization under Heavy-tailed Payoffs. 13790-13801 - Victor Garcia Satorras, Max Welling, Zeynep Akata:
Combining Generative and Discriminative Models for Hybrid Inference. 13802-13812 - Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra:
A Graph Theoretic Additive Approximation of Optimal Transport. 13813-13823 - Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. 13824-13833 - Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar:
Sampled Softmax with Random Fourier Features. 13834-13844 - Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka:
Semi-flat minima and saddle points by embedding neural networks to overparameterization. 13845-13853 - Jiechuan Jiang, Zongqing Lu:
Learning Fairness in Multi-Agent Systems. 13854-13865 - Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis:
Primal-Dual Block Generalized Frank-Wolfe. 13866-13875 - Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard:
GOT: An Optimal Transport framework for Graph comparison. 13876-13887 - Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak:
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. 13888-13899 - Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Complexity of Highly Parallel Non-Smooth Convex Optimization. 13900-13909 - Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis:
Inverting Deep Generative models, One layer at a time. 13910-13919 - Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann:
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization. 13920-13931 - Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov:
The Implicit Metropolis-Hastings Algorithm. 13932-13942 - Mehmet Fatih Sahin, Armin Eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints. 13943-13955 - Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. 13956-13968 - Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado:
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. 13969-13980 - Vickram Rajendran, William LeVine:
Accurate Layerwise Interpretable Competence Estimation. 13981-13991 - Lalit Jain, Kevin G. Jamieson:
A New Perspective on Pool-Based Active Classification and False-Discovery Control. 13992-14003 - Ximing Qiao, Yukun Yang, Hai Li:
Defending Neural Backdoors via Generative Distribution Modeling. 14004-14013 - Paul Michel, Omer Levy, Graham Neubig:
Are Sixteen Heads Really Better than One? 14014-14024 - Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami:
Multi-resolution Multi-task Gaussian Processes. 14025-14035 - Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Bayesian Optimal Experimental Design. 14036-14047 - Joshua Hanson, Maxim Raginsky:
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. 14048-14058 - Matt Jordan, Justin Lewis, Alexandros G. Dimakis:
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes. 14059-14069 - Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire:
Reinforcement Learning with Convex Constraints. 14070-14079 - Dirk van der Hoeven:
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning. 14080-14089 - Johannes Kirschner, Andreas Krause:
Stochastic Bandits with Context Distributions. 14090-14099 - Dan Schwartz, Mariya Toneva, Leila Wehbe:
Inducing brain-relevant bias in natural language processing models. 14100-14110 - Harm van Seijen, Mehdi Fatemi, Arash Tavakoli:
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. 14111-14121 - Ciara Pike-Burke, Steffen Grünewälder:
Recovering Bandits. 14122-14131 - Matthew Sotoudeh, Aditya V. Thakur:
Computing Linear Restrictions of Neural Networks. 14132-14143 - Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He:
Learning Positive Functions with Pseudo Mirror Descent. 14144-14154 - Bastian Alt, Adrian Sosic, Heinz Koeppl:
Correlation Priors for Reinforcement Learning. 14155-14165 - Deeksha Adil, Richard Peng, Sushant Sachdeva:
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. 14166-14177 - Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit. 14178-14189 - Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Differentially Private Covariance Estimation. 14190-14199 - Mostafa Rahmani, Ping Li:
Outlier Detection and Robust PCA Using a Convex Measure of Innovation. 14200-14210 - Robert Osazuwa Ness, Kaushal Paneri, Olga Vitek:
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems. 14211-14221 - Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? 14222-14235 - Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. 14236-14245 - Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher
Stochastic Frank-Wolfe for Composite Convex Minimization. 14246-14256 - Honghao Li, Vincent Cabeli, Nadir Sella, Hervé Isambert:
Constraint-based Causal Structure Learning with Consistent Separating Sets. 14257-14266 - David G. Clark, Jesse Livezey, Kristofer E. Bouchard:
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. 14267-14278 - Kristjan H. Greenewald, Dmitriy Katz, Karthikeyan Shanmugam
Sample Efficient Active Learning of Causal Trees. 14279-14289 - Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan:
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection. 14290-14299 - Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha:
Robust Attribution Regularization. 14300-14310 - Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, Bill Freeman, Frédo Durand:
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization. 14311-14321 - Hado van Hasselt, Matteo Hessel, John Aslanides:
When to use parametric models in reinforcement learning? 14322-14333 - Maurice Weiler, Gabriele Cesa:
General E(2)-Equivariant Steerable CNNs. 14334-14345 - Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. 14346-14356 - Saurabh Sihag, Ali Tajer:
Structure Learning with Side Information: Sample Complexity. 14357-14367 - Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung:
Untangling in Invariant Speech Recognition. 14368-14378 - Caroline Haimerl, Cristina Savin, Eero P. Simoncelli
Flexible information routing in neural populations through stochastic comodulation. 14379-14388 - Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari:
Generalization Bounds in the Predict-then-Optimize Framework. 14389-14398 - Matthieu Jedor, Vianney Perchet, Jonathan Louëdec:
Categorized Bandits. 14399-14409 - Daniel Russo:
Worst-Case Regret Bounds for Exploration via Randomized Value Functions. 14410-14420 - Nishal P. Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan M. Litke, Liam Paninski, E. J. Chichilnisky:
Efficient characterization of electrically evoked responses for neural interfaces. 14421-14431 - Kanthi K. Sarpatwar, Karthikeyan Shanmugam
Differentially Private Distributed Data Summarization under Covariate Shift. 14432-14442 - Brendan O'Donoghue, Chris J. Maddison:
Hamiltonian descent for composite objectives. 14443-14453 - Nicolò Pagliana, Lorenzo Rosasco:
Implicit Regularization of Accelerated Methods in Hilbert Spaces. 14454-14464 - Rémy Degenne, Wouter M. Koolen, Pierre Ménard:
Non-Asymptotic Pure Exploration by Solving Games. 14465-14474 - Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai:
Implicit Posterior Variational Inference for Deep Gaussian Processes. 14475-14486 - Benyamin Allahgholizadeh Haghi, Spencer S. Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford, Daniel Kramer, Brian C. Lee, Charles Liu, Richard A. Andersen, Azita Emami:
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces. 14487-14498 - Arun Verma, Manjesh Kumar Hanawal, Arun Rajkumar, Raman Sankaran:
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback. 14499-14509 - Brandon M. Anderson, Truong-Son Hy, Risi Kondor:
Cormorant: Covariant Molecular Neural Networks. 14510-14519 - Andrey Malinin, Mark J. F. Gales:
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness. 14520-14531 - Youwei Lyu, Zhaopeng Cui, Si Li, Marc Pollefeys, Boxin Shi:
Reflection Separation using a Pair of Unpolarized and Polarized Images. 14532-14542 - Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. 14543-14553 - Alix Lhéritier, Frédéric Cazals:
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees. 14554-14563 - Rémy Degenne, Wouter M. Koolen:
Pure Exploration with Multiple Correct Answers. 14564-14573 - Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. 14574-14583 - Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. 14584-14597 - Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin:
Compiler Auto-Vectorization with Imitation Learning. 14598-14609 - Runzhe Yang, Xingyuan Sun, Karthik Narasimhan:
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation. 14610-14621 - Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson:
Exact Gaussian Processes on a Million Data Points. 14622-14632 - Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. 14633-14645 - Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas:
Learning Compositional Neural Programs with Recursive Tree Search and Planning. 14646-14656 - Nirandika Wanigasekara, Christina Lee Yu:
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric. 14657-14667 - Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi:
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. 14668-14679 - Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. 14680-14691 - Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. 14692-14701 - Zehua Zhang, Chen Yu, David J. Crandall:
A Self Validation Network for Object-Level Human Attention Estimation. 14702-14713 - Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo:
Model Selection for Contextual Bandits. 14714-14725 - Titouan Vayer, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel:
Sliced Gromov-Wasserstein. 14726-14736 - Zhiqiang Xu, Ping Li:
Towards Practical Alternating Least-Squares for CCA. 14737-14746 - Ligeng Zhu, Zhijian Liu, Song Han:
Deep Leakage from Gradients. 14747-14756 - Fanny Yang, Zuowen Wang, Christina Heinze-Deml:
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness. 14757-14768 - Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. 14769-14779 - Amir-massoud Farahmand:
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm. 14780-14790 - Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. 14791-14802 - Gauri Jagatap, Chinmay Hegde:
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors. 14803-14813 - Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine:
Planning with Goal-Conditioned Policies. 14814-14825 - Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanic:
Don't take it lightly: Phasing optical random projections with unknown operators. 14826-14836 - Ali Razavi, Aäron van den Oord, Oriol Vinyals:
Generating Diverse High-Fidelity Images with VQ-VAE-2. 14837-14847 - Pedro Mercado, Francesco Tudisco, Matthias Hein:
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. 14848-14857 - Yingying Li, Xin Chen, Na Li:
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis. 14858-14870 - Wei Ma, George H. Chen:
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption. 14871-14880 - Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brébisson, Yoshua Bengio, Aaron C. Courville:
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis. 14881-14892 - Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas:
Offline Contextual Bandits with High Probability Fairness Guarantees. 14893-14904 - Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn:
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. 14905-14916 - Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal:
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning. 14917-14927 - Mariya Toneva, Leila Wehbe:
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). 14928-14938 - Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson:
Function-Space Distributions over Kernels. 14939-14950 - Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. 14951-14962 - Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine:
Compositional Plan Vectors. 14963-14974 - Amit Daniely, Vitaly Feldman:
Locally Private Learning without Interaction Requires Separation. 14975-14986 - Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. 14987-14996 - Kunal Talwar:
Computational Separations between Sampling and Optimization. 14997-15007 - Ganlin Song, Zhou Fan, John Lafferty:
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks. 15008-15017 - Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen:
Learning to Optimize in Swarms. 15018-15028 - Liu Leqi, Adarsh Prasad, Pradeep Ravikumar:
On Human-Aligned Risk Minimization. 15029-15038 - Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis:
Semi-Parametric Efficient Policy Learning with Continuous Actions. 15039-15049 - Fariba Yousefi, Michael Thomas Smith, Mauricio A. Álvarez:
Multi-task Learning for Aggregated Data using Gaussian Processes. 15050-15060 - Bulat Ibragimov, Gleb Gusev:
Minimal Variance Sampling in Stochastic Gradient Boosting. 15061-15071 - Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev:
Beyond the Single Neuron Convex Barrier for Neural Network Certification. 15072-15083 - Firoozeh Sepehr, Donatello Materassi:
An Algorithm to Learn Polytree Networks with Hidden Nodes. 15084-15093 - Andisheh Amrollahi, Amir Zandieh, Michael Kapralov
Efficiently Learning Fourier Sparse Set Functions. 15094-15103 - Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas:
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. 15104-15113 - Rachel Carrington, Karthik Bharath, Simon Preston:
Invariance and identifiability issues for word embeddings. 15114-15123 - Xiaoyun Li, Ping Li:
Generalization Error Analysis of Quantized Compressive Learning. 15124-15134 - Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh S. Vempala:
Multi-Criteria Dimensionality Reduction with Applications to Fairness. 15135-15145 - Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Efficient Rematerialization for Deep Networks. 15146-15155 - Samuel K. Ainsworth, Matt Barnes, Siddhartha S. Srinivasa:
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation. 15156-15166 - Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis:
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. 15167-15176 - Meena Jagadeesan:
Understanding Sparse JL for Feature Hashing. 15177-15187 - Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen:
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning. 15188-15198 - Joshua Robinson, Suvrit Sra, Stefanie Jegelka:
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. 15199-15209 - Ashok Cutkosky
Momentum-Based Variance Reduction in Non-Convex SGD. 15210-15219 - Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. 15220-15231 - Nishant Subramani, Samuel R. Bowman, Kyunghyun Cho:
Can Unconditional Language Models Recover Arbitrary Sentences? 15232-15242 - Xueru Zhang, Mohammadmahdi Khaliligarekani, Cem Tekin, Mingyan Liu:
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness. 15243-15252 - Digvijay Boob, Saurabh Sawlani, Di Wang:
Faster width-dependent algorithm for mixed packing and covering LPs. 15253-15262 - Fabio Vitale, Anand Rajagopalan, Claudio Gentile:
Flattening a Hierarchical Clustering through Active Learning. 15263-15273 - Matthieu Simeoni, Sepand Kashani, Paul Hurley, Martin Vetterli:
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging. 15274-15286 - Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Certifying Geometric Robustness of Neural Networks. 15287-15297 - Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp:
Goal-conditioned Imitation Learning. 15298-15309 - Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson:
Robust exploration in linear quadratic reinforcement learning. 15310-15320 - Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang:
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs. 15321-15331 - Kwang-Sung Jun, Ashok Cutkosky
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. 15332-15341 - Melikasadat Emami, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Alyson K. Fletcher:
Input-Output Equivalence of Unitary and Contractive RNNs. 15342-15352 - Samuel Greydanus, Misko Dzamba, Jason Yosinski:
Hamiltonian Neural Networks. 15353-15363 - Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen:
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. 15364-15376 - Dina Obeid, Hugo Ramambason, Cengiz Pehlevan:
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. 15377-15386 - Nima Dehmamy, Albert-László Barabási, Rose Yu:
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. 15387-15397 - Yichuan Charlie Tang, Ruslan Salakhutdinov:
Multiple Futures Prediction. 15398-15408 - Tristan Bepler, Ellen D. Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger:
Explicitly disentangling image content from translation and rotation with spatial-VAE. 15409-15419 - Jingbo Liu, Philippe Rigollet:
Power analysis of knockoff filters for correlated designs. 15420-15429 - Yihao Feng, Lihong Li, Qiang Liu:
A Kernel Loss for Solving the Bellman Equation. 15430-15441 - Jonas Mueller, Vasilis Syrgkanis, Matt Taddy:
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. 15442-15452 - Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov:
Differential Privacy Has Disparate Impact on Model Accuracy. 15453-15462 - Daniel A. Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord:
Riemannian batch normalization for SPD neural networks. 15463-15474 - Aria Y. Wang, Michael J. Tarr, Leila Wehbe:
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity. 15475-15485 - Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton:
Stacked Capsule Autoencoders. 15486-15496 - Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Anthony Valenzano, Margarita P. Castro, Sheila A. McIlraith:
Learning Reward Machines for Partially Observable Reinforcement Learning. 15497-15508 - Philip Bachman, R. Devon Hjelm, William Buchwalter:
Learning Representations by Maximizing Mutual Information Across Views. 15509-15519 - Sam Wiseman, Yoon Kim:
Amortized Bethe Free Energy Minimization for Learning MRFs. 15520-15531 - Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. 15532-15543 - Aaron Voelker, Ivana Kajic, Chris Eliasmith:
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks. 15544-15553 - Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi:
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. 15554-15566 - Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan:
Fast structure learning with modular regularization. 15567-15577 - Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aäron van den Oord, Sergey Levine, Pierre Sermanet:
Wasserstein Dependency Measure for Representation Learning. 15578-15588 - Jingxiang Lin, Unnat Jain, Alexander G. Schwing:
TAB-VCR: Tags and Attributes based VCR Baselines. 15589-15602 - Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. 15603-15615 - Raphael J. L. Townshend, Rishi Bedi, Patricia Suriana, Ron O. Dror:
End-to-End Learning on 3D Protein Structure for Interface Prediction. 15616-15625 - Yingdong Lu, Mark S. Squillante, Chai Wah Wu:
A Family of Robust Stochastic Operators for Reinforcement Learning. 15626-15636 - Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song:
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. 15637-15648 - Han Zhao, Geoffrey J. Gordon:
Inherent Tradeoffs in Learning Fair Representations. 15649-15659 - Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Are deep ResNets provably better than linear predictors? 15660-15669 - Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. 15670-15679 - Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey E. Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski:
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. 15680-15691 - Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr:
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. 15692-15703 - Michalis K. Titsias, Petros Dellaportas:
Gradient-based Adaptive Markov Chain Monte Carlo. 15704-15713 - Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. 15714-15725 - Abhinav Verma
Imitation-Projected Programmatic Reinforcement Learning. 15726-15737 - Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, Eric P. Xing:
Learning Data Manipulation for Augmentation and Weighting. 15738-15749 - Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe:
Exploring Algorithmic Fairness in Robust Graph Covering Problems. 15750-15761 - Pavithra Prabhakar, Zahra Rahimi Afzal:
Abstraction based Output Range Analysis for Neural Networks. 15762-15772 - Arturs Backurs, Piotr Indyk, Tal Wagner:
Space and Time Efficient Kernel Density Estimation in High Dimensions. 15773-15782 - Parikshit Gopalan, Vatsal Sharan, Udi Wieder:
PIDForest: Anomaly Detection via Partial Identification. 15783-15793 - John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. 15794-15805 - Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk:
The Geometry of Deep Networks: Power Diagram Subdivision. 15806-15815 - Ke Li, Tianhao Zhang, Jitendra Malik:
Approximate Feature Collisions in Neural Nets. 15816-15824 - Fushan Li, Michael Bowling:
Ease-of-Teaching and Language Structure from Emergent Communication. 15825-15835 - Anthony Ndirango, Tyler Lee:
Generalization in multitask deep neural classifiers: a statistical physics approach. 15836-15845 - Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann:
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation. 15846-15856 - Alexey Ignatiev, Nina Narodytska, João Marques-Silva:
On Relating Explanations and Adversarial Examples. 15857-15867 - Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna:
On the equivalence between graph isomorphism testing and function approximation with GNNs. 15868-15876 - Hosein Hasani, Mahdieh Soleymani, Hamid Aghajan:
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks. 15877-15888 - Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan:
Self-attention with Functional Time Representation Learning. 15889-15899 - Ping Li, Xiaoyun Li, Cun-Hui Zhang:
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling. 15900-15910 - Mohammad Reza Keshtkaran, Chethan Pandarinath:
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data. 15911-15921 - Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V. Le:
Mixtape: Breaking the Softmax Bottleneck Efficiently. 15922-15930
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