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24th AISTATS 2021: Virtual Event
- Arindam Banerjee, Kenji Fukumizu:
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event. Proceedings of Machine Learning Research 130, PMLR 2021 - Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On the Effect of Auxiliary Tasks on Representation Dynamics. 1-9 - Ismael Lemhadri, Feng Ruan, Robert Tibshirani:
LassoNet: Neural Networks with Feature Sparsity. 10-18 - Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta:
Projection-Free Optimization on Uniformly Convex Sets. 19-27 - Shinsaku Sakaue:
Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications. 28-36 - Antoine Wehenkel, Gilles Louppe:
Graphical Normalizing Flows. 37-45 - Yajie Bao
, Weijia Xiong:
One-Round Communication Efficient Distributed M-Estimation. 46-54 - Valerii Likhosherstov, Jared Davis, Krzysztof Choromanski, Adrian Weller:
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices. 55-63 - Hisham Husain, Kamil Ciosek, Ryota Tomioka:
Regularized Policies are Reward Robust. 64-72 - Taihong Xiao, Xinyu Zhang, Hao-Lin Jia, Ming-Ming Cheng, Ming-Hsuan Yang:
Semi-Supervised Learning with Meta-Gradient. 73-81 - Sattar Vakili, Kia Khezeli, Victor Picheny:
On Information Gain and Regret Bounds in Gaussian Process Bandits. 82-90 - Daniel Hsu, Vidya Muthukumar, Ji Xu:
On the proliferation of support vectors in high dimensions. 91-99 - Nikhil Mehta, Kevin J. Liang, Vinay Kumar Verma, Lawrence Carin:
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors. 100-108 - Elchanan Solomon, Alexander Wagner, Paul Bendich:
A Fast and Robust Method for Global Topological Functional Optimization. 109-117 - Sida Peng, Yang Ning:
Regression Discontinuity Design under Self-selection. 118-126 - Ziping Xu, Amirhossein Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. 127-135 - Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. 136-144 - Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima:
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. 145-153 - Naoto Ohsaka:
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. 154-162 - Lu Yu, Tobias Kaufmann, Johannes Lederer:
False Discovery Rates in Biological Networks. 163-171 - Horace Pan, Risi Kondor:
Fourier Bases for Solving Permutation Puzzles. 172-180 - Feynman T. Liang, Nimar S. Arora, Nazanin Khosravani Tehrani, Yucen Lily Li, Michael Tingley, Erik Meijer:
Accelerating Metropolis-Hastings with Lightweight Inference Compilation. 181-189 - Xing Han, Sambarta Dasgupta, Joydeep Ghosh:
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series. 190-198 - Jiaqi Yang:
Fully Gap-Dependent Bounds for Multinomial Logit Bandit. 199-207 - Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn:
Alternating Direction Method of Multipliers for Quantization. 208-216 - Yuantong Li, Chi-Hua Wang, Guang Cheng:
Online Forgetting Process for Linear Regression Models. 217-225 - Emanuele Dolera, Stefano Favaro, Stefano Peluchetti:
A Bayesian nonparametric approach to count-min sketch under power-law data streams. 226-234 - Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc:
Nonlinear Functional Output Regression: A Dictionary Approach. 235-243 - Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
When MAML Can Adapt Fast and How to Assist When It Cannot. 244-252 - Xavier Gitiaux, Huzefa Rangwala:
Learning Smooth and Fair Representations. 253-261 - Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. 262-270 - Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai:
Contextual Blocking Bandits. 271-279 - Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. 280-288 - HaiYing Wang, Jiahui Zou:
A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling. 289-297 - Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. 298-306 - Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlas, Johannes Rausch, Ce Zhang, Andreas Krause:
Online Active Model Selection for Pre-trained Classifiers. 307-315 - Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. 316-324 - Ting-Han Fan, Peter J. Ramadge:
A Contraction Approach to Model-based Reinforcement Learning. 325-333 - Tomohiro Hayase
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry. 334-342 - Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves
Benchmarking Simulation-Based Inference. 343-351 - Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. 352-360 - Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis:
Deep Spectral Ranking. 361-369 - Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou:
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits. 370-378 - Yingjie Bi, Javad Lavaei:
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems. 379-387 - Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. 388-396 - Marco Mondelli, Ramji Venkataramanan:
Approximate Message Passing with Spectral Initialization for Generalized Linear Models. 397-405 - Weishi Shi, Qi Yu
Active Learning with Maximum Margin Sparse Gaussian Processes. 406-414 - Wenkai Xu, Gesine Reinert:
A Stein Goodness-of-test for Exponential Random Graph Models. 415-423 - François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz:
The Sample Complexity of Level Set Approximation. 424-432 - Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes:
Curriculum Learning by Optimizing Learning Dynamics. 433-441 - Ugo Tanielian, Gérard Biau:
Approximating Lipschitz continuous functions with GroupSort neural networks. 442-450 - Daniel Augusto de Souza, Diego P. P. Mesquita, João Paulo Pordeus Gomes, César Lincoln C. Mattos:
Learning GPLVM with arbitrary kernels using the unscented transformation. 451-459 - Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. 460-468 - Kai Brügge, Asja Fischer, Christian Igel:
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. 469-477 - Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. 478-486 - Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác:
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. 487-495 - Yue Xing, Qifan Song, Guang Cheng:
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. 496-504 - Yue Xing, Qifan Song, Guang Cheng:
On the Generalization Properties of Adversarial Training. 505-513 - Yue Xing, Ruizhi Zhang, Guang Cheng:
Adversarially Robust Estimate and Risk Analysis in Linear Regression. 514-522 - Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári:
Adaptive Approximate Policy Iteration. 523-531 - Guillaume Ausset, Stéphan Clémençon, François Portier:
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. 532-540 - Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. 541-549 - Damien Scieur, Lewis Liu, Thomas Pumir, Nicolas Boumal:
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates. 550-558 - Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar:
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering. 559-567 - Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang:
Generalization Bounds for Stochastic Saddle Point Problems. 568-576 - Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defend by Learning to Attack. 577-585 - Benwei Shi, Jeff M. Phillips:
A Deterministic Streaming Sketch for Ridge Regression. 586-594 - Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao:
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems. 595-603 - Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy:
On the role of data in PAC-Bayes. 604-612 - Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin:
CADA: Communication-Adaptive Distributed Adam. 613-621 - Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna P. Jagannathan:
Bandit algorithms: Letting go of logarithmic regret for statistical robustness. 622-630 - Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf:
Geometrically Enriched Latent Spaces. 631-639 - Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári:
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. 640-648 - Fanghui Liu, Zhenyu Liao
Kernel regression in high dimensions: Refined analysis beyond double descent. 649-657 - Yoan Russac, Louis Faury, Olivier Cappé, Aurélien Garivier:
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting. 658-666 - Lucas Cassano, Ali H. Sayed:
Logical Team Q-learning: An approach towards factored policies in cooperative MARL. 667-675 - Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven:
Automatic structured variational inference. 676-684 - Victor Garcia Satorras, Max Welling:
Neural Enhanced Belief Propagation on Factor Graphs. 685-693 - Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. 694-702 - Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural nets via local linearization. 703-711 - Samir Chowdhury, Tom Needham:
Generalized Spectral Clustering via Gromov-Wasserstein Learning. 712-720 - Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. 721-729 - David Eriksson, Matthias Poloczek:
Scalable Constrained Bayesian Optimization. 730-738 - Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner:
Sample efficient learning of image-based diagnostic classifiers via probabilistic labels. 739-747 - Jason M. Klusowski, Peter M. Tian:
Nonparametric Variable Screening with Optimal Decision Stumps. 748-756 - Jason M. Klusowski:
Sharp Analysis of a Simple Model for Random Forests. 757-765 - Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele:
Nested Barycentric Coordinate System as an Explicit Feature Map. 766-774 - Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien:
An Analysis of the Adaptation Speed of Causal Models. 775-783 - Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. 784-792 - Yongchan Kwon, Manuel A. Rivas, James Zou:
Efficient Computation and Analysis of Distributional Shapley Values. 793-801 - John C. Duchi, Feng Ruan:
A constrained risk inequality for general losses. 802-810 - Tengyu Xu, Yingbin Liang:
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. 811-819 - Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang:
Learning Prediction Intervals for Regression: Generalization and Calibration. 820-828 - Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng:
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. 829-837 - Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. 838-846 - Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. 847-855 - Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie:
Deep Fourier Kernel for Self-Attentive Point Processes. 856-864 - Matthew Holland:
Robustness and scalability under heavy tails, without strong convexity. 865-873 - Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin:
Understanding the wiring evolution in differentiable neural architecture search. 874-882 - Zhiyu Zhang, Ioannis Ch. Paschalidis:
Provable Hierarchical Imitation Learning via EM. 883-891 - Matthew J. Holland, El Mehdi Haress:
Learning with risk-averse feedback under potentially heavy tails. 892-900 - Vo Nguyen Le Duy, Ichiro Takeuchi:
Parametric Programming Approach for More Powerful and General Lasso Selective Inference. 901-909 - Yanjun Han:
On the High Accuracy Limitation of Adaptive Property Estimation. 910-918 - Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. 919-927 - Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei:
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. 928-936 - Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. 937-945 - Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. 946-954 - Shunsuke Horii:
Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions. 955-963 - Francesco Quinzan, Vanja Doskoc, Andreas Göbel, Tobias Friedrich:
Adaptive Sampling for Fast Constrained Maximization of Submodular Functions. 964-972 - Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi:
Mean-Variance Analysis in Bayesian Optimization under Uncertainty. 973-981 - Fan Wu
Hadamard Wirtinger Flow for Sparse Phase Retrieval. 982-990 - Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett:
Stochastic Linear Bandits Robust to Adversarial Attacks. 991-999 - Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning. 1000-1008 - Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho:
Optimizing Percentile Criterion using Robust MDPs. 1009-1017 - Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. 1018-1026 - Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates:
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. 1027-1035 - Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. 1036-1044 - Mohamed El Amine Seddik, Cosme Louart, Romain Couillet, Mohamed Tamaazousti:
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. 1045-1053 - Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates:
Measure Transport with Kernel Stein Discrepancy. 1054-1062 - Chuanhao Li, Qingyun Wu, Hongning Wang:
Unifying Clustered and Non-stationary Bandits. 1063-1071 - Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier:
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. 1072-1080 - Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas:
Transforming Gaussian Processes With Normalizing Flows. 1081-1089 - Markus Lange-Hegermann:
Linearly Constrained Gaussian Processes with Boundary Conditions. 1090-1098 - Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic:
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. 1099-1107 - Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez:
Top-m identification for linear bandits. 1108-1116 - Ziwei Guan, Tengyu Xu, Yingbin Liang:
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. 1117-1125 - Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom:
Online k-means Clustering. 1126-1134 - Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian:
Consistent k-Median: Simpler, Better and Robust. 1135-1143 - Min Wen, Osbert Bastani, Ufuk Topcu:
Algorithms for Fairness in Sequential Decision Making. 1144-1152 - Abhin Shah, Devavrat Shah, Gregory W. Wornell:
On Learning Continuous Pairwise Markov Random Fields. 1153-1161 - Kishor Jothimurugan, Osbert Bastani, Rajeev Alur:
Abstract Value Iteration for Hierarchical Reinforcement Learning. 1162-1170 - Jalaj Upadhyay, Sarvagya Upadhyay, Raman Arora:
Differentially Private Analysis on Graph Streams. 1171-1179 - Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller:
Learning with Hyperspherical Uniformity. 1180-1188 - Ioannis C. Tsaknakis, Mingyi Hong:
Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function. 1189-1197 - Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters:
Latent Derivative Bayesian Last Layer Networks. 1198-1206 - Nhuong V. Nguyen, Toan N. Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam M. Nguyen, Marten van Dijk:
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. 1207-1215 - Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter:
Provably Safe PAC-MDP Exploration Using Analogies. 1216-1224 - Guanyang Wang, John O'Leary, Pierre Jacob:
Maximal Couplings of the Metropolis-Hastings Algorithm. 1225-1233 - Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. 1234-1242 - Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie:
Goodness-of-Fit Test for Mismatched Self-Exciting Processes. 1243-1251 - Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman:
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship. 1252-1260 - Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas:
A Study of Condition Numbers for First-Order Optimization. 1261-1269 - Kartik Ahuja, Karthikeyan Shanmugam
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. 1270-1278 - Sebastian Perez-Salazar, Rachel Cummings:
Differentially Private Online Submodular Maximization. 1279-1287 - Quentin Bertrand, Mathurin Massias:
Anderson acceleration of coordinate descent. 1288-1296 - Nick Whiteley, Lorenzo Rimella:
Inference in Stochastic Epidemic Models via Multinomial Approximations. 1297-1305 - Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien:
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. 1306-1314 - Robert M. Gower, Othmane Sebbouh, Nicolas Loizou:
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. 1315-1323 - Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. 1324-1332 - Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt:
Latent variable modeling with random features. 1333-1341 - Will Ma, David Simchi-Levi:
Reaping the Benefits of Bundling under High Production Costs. 1342-1350 - Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
Momentum Improves Optimization on Riemannian Manifolds. 1351-1359 - Alan Kuhnle:
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time. 1360-1368 - Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar:
On Data Efficiency of Meta-learning. 1369-1377 - Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. 1378-1386 - Yuyang Deng, Mehrdad Mahdavi:
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency. 1387-1395 - Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits. 1396-1404 - Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. 1405-1413 - Zhuo Sun, Jijie Wu, Xiaoxu Li, Wenming Yang, Jing-Hao Xue:
Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification. 1414-1422 - Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey:
Tractable contextual bandits beyond realizability. 1423-1431 - Wasim Huleihel, Soumyabrata Pal, Ofer Shayevitz:
Learning User Preferences in Non-Stationary Environments. 1432-1440 - Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang:
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes. 1441-1449 - Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas:
Efficient Statistics for Sparse Graphical Models from Truncated Samples. 1450-1458 - Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath:
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. 1459-1467 - Gregory Canal, Matthieu R. Bloch, Christopher Rozell:
Feedback Coding for Active Learning. 1468-1476 - Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese:
Shadow Manifold Hamiltonian Monte Carlo. 1477-1485 - Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal:
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms. 1486-1494 - Yunfeng Cai, Ping Li:
Identification of Matrix Joint Block Diagonalization. 1495-1503 - Jiang Qian, Yuren Wu, Bojin Zhuang, Shaojun Wang, Jing Xiao:
Understanding Gradient Clipping In Incremental Gradient Methods. 1504-1512 - Changhee Lee, Mihaela van der Schaar:
A Variational Information Bottleneck Approach to Multi-Omics Data Integration. 1513-1521 - Zinan Lin, Vyas Sekar, Giulia Fanti:
On the Privacy Properties of GAN-generated Samples. 1522-1530 - Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri:
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. 1531-1539 - Avrim Blum, Chen Dan, Saeed Seddighin:
Learning Complexity of Simulated Annealing. 1540-1548 - Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen:
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process. 1549-1557 - Lunjia Hu, Omer Reingold:
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. 1558-1566 - Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. 1567-1575 - Kunhe Yang, Lin F. Yang
Q-learning with Logarithmic Regret. 1576-1584 - Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. 1585-1593 - Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhi-Quan Luo:
Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. 1594-1602 - Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen:
Robust and Private Learning of Halfspaces. 1603-1611 - Cameron Voloshin, Nan Jiang, Yisong Yue:
Minimax Model Learning. 1612-1620 - Zhiqiang Xu, Ping Li:
On the Faster Alternating Least-Squares for CCA. 1621-1629 - Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl:
Exploiting Equality Constraints in Causal Inference. 1630-1638 - Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas:
Collaborative Classification from Noisy Labels. 1639-1647 - Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. 1648-1656 - Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian:
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT. 1657-1665 - Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng:
Why did the distribution change? 1666-1674 - Hadi Mohasel Afshar, Rafael Oliveira, Sally Cripps:
Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers. 1675-1683 - Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa:
Iterative regularization for convex regularizers. 1684-1692 - Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou:
Competing AI: How does competition feedback affect machine learning? 1693-1701 - Xiaotong Yuan, Ping Li:
Stability and Risk Bounds of Iterative Hard Thresholding. 1702-1710 - Yuki Ohnishi, Jean Honorio
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation. 1711-1719 - Youssef Mroueh, Truyen Nguyen:
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow. 1720-1728 - Qiming Du, Gérard Biau, François Petit, Raphaël Porcher:
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects. 1729-1737 - Darina Dvinskikh, Daniil Tiapkin:
Improved Complexity Bounds in Wasserstein Barycenter Problem. 1738-1746 - William J. Wilkinson, Arno Solin, Vincent Adam:
Sparse Algorithms for Markovian Gaussian Processes. 1747-1755 - Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal:
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. 1756-1764 - Nina Vesseron, Ievgen Redko, Charlotte Laclau:
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond. 1765-1773 - Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron:
All of the Fairness for Edge Prediction with Optimal Transport. 1774-1782 - Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. 1783-1791 - Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen:
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. 1792-1800 - Marek Wydmuch, Kalina Jasinska-Kobus, Devanathan Thiruvenkatachari, Krzysztof Dembczynski:
Online probabilistic label trees. 1801-1809 - Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. 1810-1818 - Frederik Harder, Kamil Adamczewski, Mijung Park:
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. 1819-1827 - Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf:
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. 1828-1836 - Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. 1837-1845 - Yann Fraboni, Richard Vidal, Marco Lorenzi:
Free-rider Attacks on Model Aggregation in Federated Learning. 1846-1854 - Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard:
Reinforcement Learning in Parametric MDPs with Exponential Families. 1855-1863 - Mike Laszkiewicz, Asja Fischer, Johannes Lederer:
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. 1864-1872 - Sayak Ray Chowdhury, Aditya Gopalan:
No-regret Algorithms for Multi-task Bayesian Optimization. 1873-1881 - Anant Raj, Francis R. Bach:
Explicit Regularization of Stochastic Gradient Methods through Duality. 1882-1890 - Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao:
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. 1891-1899 - Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath:
CONTRA: Contrarian statistics for controlled variable selection. 1900-1908 - Kai Cui, Heinz Koeppl:
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning. 1909-1917 - Christian Wildner, Heinz Koeppl:
Moment-Based Variational Inference for Stochastic Differential Equations. 1918-1926 - Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka:
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. 1927-1935 - Moritz Wolter, Jochen Garcke:
Adaptive wavelet pooling for convolutional neural networks. 1936-1944 - Fergus Simpson, Alexis Boukouvalas, Václav Cadek, Elvijs Sarkans, Nicolas Durrande:
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain. 1945-1953 - Shingo Yashima, Atsushi Nitanda, Taiji Suzuki:
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. 1954-1962 - Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard:
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding. 1963-1971 - Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin:
Counterfactual Representation Learning with Balancing Weights. 1972-1980 - Tomoya Murata, Taiji Suzuki:
Gradient Descent in RKHS with Importance Labeling. 1981-1989 - Dominic Richards, Mike Rabbat:
Learning with Gradient Descent and Weakly Convex Losses. 1990-1998 - Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi:
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. 1999-2007 - Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou:
Approximate Data Deletion from Machine Learning Models. 2008-2016 - Vineet Nair, Vishakha Patil, Gaurav Sinha:
Budgeted and Non-Budgeted Causal Bandits. 2017-2025 - Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying:
Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. 2026-2034 - Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi:
Equitable and Optimal Transport with Multiple Agents. 2035-2043 - Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran:
A Variational Inference Approach to Learning Multivariate Wold Processes. 2044-2052 - Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang:
Active Online Learning with Hidden Shifting Domains. 2053-2061 - Abhimanyu Dubey:
No-Regret Algorithms for Private Gaussian Process Bandit Optimization. 2062-2070 - Akash Kumar, Hanqi Zhang, Adish Singla
The Teaching Dimension of Kernel Perceptron. 2071-2079 - Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots:
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. 2080-2088 - Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu:
Localizing Changes in High-Dimensional Regression Models. 2089-2097 - Guodong Zhang, Yuanhao Wang:
On the Suboptimality of Negative Momentum for Minimax Optimization. 2098-2106 - Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe:
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. 2107-2115 - Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri:
Corralling Stochastic Bandit Algorithms. 2116-2124 - Nitesh Kumar, Ondrej Kuzelka:
Context-Specific Likelihood Weighting. 2125-2133 - Can Xu
Learning Matching Representations for Individualized Organ Transplantation Allocation. 2134-2142 - Nicole Mücke:
Stochastic Gradient Descent Meets Distribution Regression. 2143-2151 - Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. 2152-2160 - Annie Marsden, John C. Duchi, Gregory Valiant:
Misspecification in Prediction Problems and Robustness via Improper Learning. 2161-2169 - Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. 2170-2178 - Aditya Gangrade, Anil Kag, Venkatesh Saligrama:
Selective Classification via One-Sided Prediction. 2179-2187 - Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. 2188-2196 - Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar:
vqSGD: Vector Quantized Stochastic Gradient Descent. 2197-2205 - Yusha Liu, Yining Wang, Aarti Singh:
Smooth Bandit Optimization: Generalization to Holder Space. 2206-2214 - Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
List Learning with Attribute Noise. 2215-2223 - Kristjan H. Greenewald, Karthikeyan Shanmugam
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. 2224-2232 - Mert Gürbüzbalaban, Yuanhan Hu:
Fractional moment-preserving initialization schemes for training deep neural networks. 2233-2241 - Yves-Laurent Kom Samo:
Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach. 2242-2250 - Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie J. Su:
Federated f-Differential Privacy. 2251-2259 - Guillaume Braun, Hemant Tyagi, Christophe Biernacki:
Clustering multilayer graphs with missing nodes. 2260-2268 - Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien:
Implicit Regularization via Neural Feature Alignment. 2269-2277 - Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos:
Aggregating Incomplete and Noisy Rankings. 2278-2286 - Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos:
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models. 2287-2295 - Jiaming Xu, Kuang Xu, Dana Yang:
Optimal query complexity for private sequential learning against eavesdropping. 2296-2304 - Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro:
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. 2305-2313 - Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser:
Differentiable Causal Discovery Under Unmeasured Confounding. 2314-2322 - Zhanyu Wang, Jean Honorio
The Sample Complexity of Meta Sparse Regression. 2323-2331 - Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu:
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. 2332-2340 - Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo, Christina Fragouli, Suhas N. Diggavi:
Group testing for connected communities. 2341-2349 - Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi:
Federated Learning with Compression: Unified Analysis and Sharp Guarantees. 2350-2358 - Marissa Connor, Gregory Canal, Christopher Rozell:
Variational Autoencoder with Learned Latent Structure. 2359-2367 - Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. 2368-2376 - Yu Gong, Hossein Hajimirsadeghi, Jiawei He, Thibaut Durand, Greg Mori:
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data. 2377-2385 - Jalaj Bhandari, Daniel Russo:
On the Linear Convergence of Policy Gradient Methods for Finite MDPs. 2386-2394 - F. Richard Guo, Emilija Perkovic:
Minimal enumeration of all possible total effects in a Markov equivalence class. 2395-2403 - Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer:
Differentially Private Weighted Sampling. 2404-2412 - Heinrich Jiang, Qijia Jiang, Aldo Pacchiano:
Learning the Truth From Only One Side of the Story. 2413-2421 - Yixing Zhang, Xiuyuan Cheng, Galen Reeves:
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples. 2422-2430 - Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang, Marta Kwiatkowska:
Bayesian Inference with Certifiable Adversarial Robustness. 2431-2439 - Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors. 2440-2448 - Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David B. Dunson, Debdeep Pati:
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference. 2449-2457 - Ki-Yeob Lee, Desik Rengarajan, Dileep M. Kalathil, Srinivas Shakkottai:
Reinforcement Learning for Mean Field Games with Strategic Complementarities. 2458-2466 - Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum:
Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. 2467-2475 - Chaoqi Wang, Shengyang Sun, Roger B. Grosse:
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? 2476-2484 - He Jiang, Ery Arias-Castro:
On the Consistency of Metric and Non-Metric K-Medoids. 2485-2493 - Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed:
Non-Stationary Off-Policy Optimization. 2494-2502 - Paxton Turner, Jingbo Liu, Philippe Rigollet:
Efficient Interpolation of Density Estimators. 2503-2511 - Paxton Turner, Jingbo Liu, Philippe Rigollet:
A Statistical Perspective on Coreset Density Estimation. 2512-2520 - Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Differential Privacy in Federated Learning. 2521-2529 - Haider Al-Tahan, Yalda Mohsenzadeh:
CLAR: Contrastive Learning of Auditory Representations. 2530-2538 - My Phan, David Arbour, Drew Dimmery, Anup B. Rao:
Designing Transportable Experiments Under S-admissability. 2539-2547 - Gauthier Gidel, David Balduzzi, Wojciech Czarnecki, Marta Garnelo, Yoram Bachrach:
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets. 2548-2556 - Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik:
Private optimization without constraint violations. 2557-2565 - Yadi Wei, Rishit Sheth, Roni Khardon:
Direct Loss Minimization for Sparse Gaussian Processes. 2566-2574 - Zachary Charles, Jakub Konecný:
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. 2575-2583 - Wei Tang, Chien-Ju Ho, Yang Liu:
Linear Models are Robust Optimal Under Strategic Behavior. 2584-2592 - Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky
Matérn Gaussian Processes on Graphs. 2593-2601 - Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani:
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. 2602-2610 - Adarsh Subbaswamy, Roy Adams, Suchi Saria:
Evaluating Model Robustness and Stability to Dataset Shift. 2611-2619 - Shashank Singh:
Continuum-Armed Bandits: A Function Space Perspective. 2620-2628 - Oron Sabag, Babak Hassibi:
Regret-Optimal Filtering. 2629-2637 - Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta:
Evading the Curse of Dimensionality in Unconstrained Private GLMs. 2638-2646 - Xiaoyun Li, Ping Li:
One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements. 2647-2655 - Ather Gattami, Qinbo Bai, Vaneet Aggarwal:
Reinforcement Learning for Constrained Markov Decision Processes. 2656-2664 - Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena:
Principal Component Regression with Semirandom Observations via Matrix Completion. 2665-2673 - Sho Sonoda, Isao Ishikawa, Masahiro Ikeda:
Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum. 2674-2682 - Shengjia Zhao, Stefano Ermon:
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. 2683-2691 - Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang:
Sample Elicitation. 2692-2700 - Zhiyan Ding, Qin Li, Jianfeng Lu, Stephen J. Wright:
Random Coordinate Underdamped Langevin Monte Carlo. 2701-2709 - Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Influence Decompositions For Neural Network Attribution. 2710-2718 - Sanmitra Ghosh, Paul Birrell, Daniela De Angelis:
Variational inference for nonlinear ordinary differential equations. 2719-2727 - Moses Charikar, Lunjia Hu:
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. 2728-2736 - Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. 2737-2745 - Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. 2746-2754 - Edmond Cunningham, Madalina Fiterau:
A Change of Variables Method For Rectangular Matrix-Vector Products. 2755-2763 - Yufeng Zhang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case. 2764-2772 - Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis:
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions. 2773-2781 - Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo:
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. 2782-2790 - Kyra Gan, Andrew A. Li, Zachary Chase Lipton, Sridhar R. Tayur:
Causal Inference with Selectively Deconfounded Data. 2791-2799 - Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott W. Linderman:
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. 2800-2808 - Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania:
Mirror Descent View for Neural Network Quantization. 2809-2817 - Aditya Bhaskara, Ashok Cutkosky
Power of Hints for Online Learning with Movement Costs. 2818-2826 - Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. 2827-2835 - Shubhanshu Shekhar, Tara Javidi
Significance of Gradient Information in Bayesian Optimization. 2836-2844 - Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou:
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. 2845-2853 - Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum:
DAG-Structured Clustering by Nearest Neighbors. 2854-2862 - Yunhao Tang, Alp Kucukelbir:
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. 2863-2871 - Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan:
Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. 2872-2880 - Casey Meehan, Kamalika Chaudhuri:
Location Trace Privacy Under Conditional Priors. 2881-2889 - Konstantin Voevodski
Large Scale K-Median Clustering for Stable Clustering Instances. 2890-2898 - Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang:
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. 2899-2907 - Omid Sadeghi, Maryam Fazel:
Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints. 2908-2916 - Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. 2917-2925 - Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham:
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. 2926-2934 - Yifan Chen, Yun Yang:
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression. 2935-2943 - Tianyu Ding, Zhihui Zhu, Manolis C. Tsakiris, René Vidal, Daniel P. Robinson:
Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. 2944-2952 - Yifan Chen, Yun Yang:
Accumulations of Projections - A Unified Framework for Random Sketches in Kernel Ridge Regression. 2953-2961 - Ananda Theertha Suresh:
Robust hypothesis testing and distribution estimation in Hellinger distance. 2962-2970 - Mohit Yadav, Daniel Sheldon, Cameron Musco:
Faster Kernel Interpolation for Gaussian Processes. 2971-2979 - Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. 2980-2988 - Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif Khan, Liheng Ma, Mark Coates:
Detection and Defense of Topological Adversarial Attacks on Graphs. 2989-2997 - Eren Ozbay, Vijay Kamble:
Training a Single Bandit Arm. 2998-3006 - Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain:
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. 3007-3015 - Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula:
Multi-Armed Bandits with Cost Subsidy. 3016-3024 - Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze-Yun Leong:
Causal Modeling with Stochastic Confounders. 3025-3033 - Fulton Wang, Ali Pinar:
The Multiple Instance Learning Gaussian Process Probit Model. 3034-3042 - Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan:
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. 3043-3051 - Ruitu Xu, Lin Chen, Amin Karbasi:
Meta Learning in the Continuous Time Limit. 3052-3060 - Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin J. Stromme:
Fast and Smooth Interpolation on Wasserstein Space. 3061-3069 - David Arbour, Drew Dimmery, Anup B. Rao:
Efficient Balanced Treatment Assignments for Experimentation. 3070-3078 - Jacob Miller, Guillaume Rabusseau, John Terilla:
Tensor Networks for Probabilistic Sequence Modeling. 3079-3087 - Andrew Wagenmaker, Julian Katz-Samuels, Kevin G. Jamieson:
Experimental Design for Regret Minimization in Linear Bandits. 3088-3096 - Shiyun Xu, Zhiqi Bu:
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. 3097-3105 - Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate
Dynamic Cutset Networks. 3106-3114 - Hamed Omidvar, Vahideh Akhlaghi, Hao Su, Massimo Franceschetti, Rajesh K. Gupta:
Associative Convolutional Layers. 3115-3123 - Jeremiah Z. Liu:
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon. 3124-3132 - Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson:
Kernel Interpolation for Scalable Online Gaussian Processes. 3133-3141 - Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li:
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks. 3142-3150 - Bingbin Liu, Pradeep Ravikumar, Andrej Risteski:
Contrastive learning of strong-mixing continuous-time stochastic processes. 3151-3159 - Chengrun Yang, Lijun Ding, Ziyang Wu, Madeleine Udell:
TenIPS: Inverse Propensity Sampling for Tensor Completion. 3160-3168 - Rina Panigrahy, Xin Wang, Manzil Zaheer:
Sketch based Memory for Neural Networks. 3169-3177 - Pratik Patil, Yuting Wei, Alessandro Rinaldo, Ryan J. Tibshirani:
Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. 3178-3186 - Zhiqi Bu, Shiyun Xu, Kan Chen:
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. 3187-3195 - Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, Stephen H. Bach:
Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees. 3196-3204 - Fan Zhou, Ping Li, Zhixin Zhou:
Principal Subspace Estimation Under Information Diffusion. 3205-3213 - Eric Chuu, Debdeep Pati, Anirban Bhattacharya:
A Hybrid Approximation to the Marginal Likelihood. 3214-3222 - Changlong Wu, Narayana Santhanam:
Prediction with Finitely many Errors Almost Surely. 3223-3231 - Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon:
Density of States Estimation for Out of Distribution Detection. 3232-3240 - Sharon Zhang, Amit Moscovich, Amit Singer:
Product Manifold Learning. 3241-3249 - Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon:
Automatic Differentiation Variational Inference with Mixtures. 3250-3258 - Anilesh Kollagunta Krishnaswamy, Zhihao Jiang, Kangning Wang, Yu Cheng, Kamesh Munagala:
Fair for All: Best-effort Fairness Guarantees for Classification. 3259-3267 - Ziwei Zhu, Wenjing Zhou:
Taming heavy-tailed features by shrinkage. 3268-3276 - Yiliang Zhang, Zhiqi Bu:
Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. 3277-3285 - Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré:
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. 3286-3294 - Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. 3295-3303 - Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic:
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization. 3304-3312 - Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian:
Understanding Robustness in Teacher-Student Setting: A New Perspective. 3313-3321 - Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld:
Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences. 3322-3330 - Zhengqing Zhou, Qinxun Bai, Zhengyuan Zhou, Linhai Qiu, Jose H. Blanchet, Peter W. Glynn:
Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning. 3331-3339 - Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. 3340-3348 - Priyank Jaini, Didrik Nielsen, Max Welling:
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. 3349-3357 - Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela:
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. 3358-3366 - Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. 3367-3375 - Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney:
Good Classifiers are Abundant in the Interpolating Regime. 3376-3384 - Vincent Zhuang, Yanan Sui:
No-Regret Reinforcement Learning with Heavy-Tailed Rewards. 3385-3393 - Meyer Scetbon, Zaïd Harchaoui:
A Spectral Analysis of Dot-product Kernels. 3394-3402 - Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, Carlee Joe-Wong:
Towards Flexible Device Participation in Federated Learning. 3403-3411 - Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue:
Active Learning under Label Shift. 3412-3420 - Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka:
Tracking Regret Bounds for Online Submodular Optimization. 3421-3429 - Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos:
Offline detection of change-points in the mean for stationary graph signals. 3430-3438 - Ali Lotfi-Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou
Hyperbolic graph embedding with enhanced semi-implicit variational inference. 3439-3447 - Tobias Schwedes, Ben Calderhead:
Rao-Blackwellised parallel MCMC. 3448-3456 - Ian Covert, Su-In Lee:
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. 3457-3465 - Nathanael Bosch, Philipp Hennig, Filip Tronarp:
Calibrated Adaptive Probabilistic ODE Solvers. 3466-3474 - Dimitar Ho, Hoang Minh Le, John Doyle, Yisong Yue:
Online Robust Control of Nonlinear Systems with Large Uncertainty. 3475-3483 - Ömer Deniz Akyildiz, Gerrit J. J. van den Burg, Theodoros Damoulas, Mark F. J. Steel:
Probabilistic Sequential Matrix Factorization. 3484-3492 - Dina Mardaoui, Damien Garreau:
An Analysis of LIME for Text Data. 3493-3501 - Prathamesh Mayekar, Ananda Theertha Suresh, Himanshu Tyagi:
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information. 3502-3510 - Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. 3511-3519 - Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen:
Causal Autoregressive Flows. 3520-3528 - Jan Achterhold
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models. 3529-3537 - Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko:
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. 3538-3546 - Anton Obukhov, Maxim V. Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool:
Spectral Tensor Train Parameterization of Deep Learning Layers. 3547-3555 - Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
Local SGD: Unified Theory and New Efficient Methods. 3556-3564 - Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth:
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. 3565-3573 - Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. 3574-3582 - Alexey Radul, Boris Alexeev:
The Base Measure Problem and its Solution. 3583-3591 - Ben Kretzu, Dan Garber:
Revisiting Projection-free Online Learning: the Strongly Convex Case. 3592-3600 - Samet Oymak, Talha Cihad Gulcu:
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models. 3601-3609 - Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu:
Logistic Q-Learning. 3610-3618 - Nafiseh Ghoroghchian, Gautam Dasarathy, Stark C. Draper:
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model. 3619-3627 - Michael Shekelyan, Graham Cormode:
Sequential Random Sampling Revisited: Hidden Shuffle Method. 3628-3636 - Kamil Adamczewski, Mijung Park:
Dirichlet Pruning for Convolutional Neural Networks. 3637-3645 - Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge:
Couplings for Multinomial Hamiltonian Monte Carlo. 3646-3654 - Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts:
Learning Bijective Feature Maps for Linear ICA. 3655-3663 - Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato:
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. 3664-3672 - Hanjing Zhu, Jiaming Xu:
One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks. 3673-3681 - Hejia Qiu, Chao Li, Ying Weng, Zhun Sun, Xingyu He, Qibin Zhao:
On the Memory Mechanism of Tensor-Power Recurrent Models. 3682-3690 - Marc Abeille, Louis Faury, Clément Calauzènes:
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits. 3691-3699 - Yunpu Ma, Volker Tresp:
Causal Inference under Networked Interference and Intervention Policy Enhancement. 3700-3708 - Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. 3709-3717 - Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki:
Latent Gaussian process with composite likelihoods and numerical quadrature. 3718-3726 - Sahra Ghalebikesabi, Rob Cornish, Chris C. Holmes, Luke J. Kelly:
Deep Generative Missingness Pattern-Set Mixture Models. 3727-3735 - Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Minimax Estimation of Laplacian Constrained Precision Matrices. 3736-3744 - Jiaxin Zhang, Sirui Bi, Guannan Zhang:
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models. 3745-3753 - Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons:
Distribution Regression for Sequential Data. 3754-3762 - Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann:
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. 3763-3771 - Sotirios-Konstantinos Anagnostidis, Aurélien Lucchi, Youssef Diouane:
Direct-Search for a Class of Stochastic Min-Max Problems. 3772-3780 - Qadeer Khan, Patrick Wenzel, Daniel Cremers:
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry. 3781-3789 - Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De:
Learning Temporal Point Processes with Intermittent Observations. 3790-3798 - Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier:
On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity. 3799-3807 - Ioannis Anagnostides, Themis Gouleakis, Ali Marashian:
Robust Learning under Strong Noise via SQs. 3808-3816 - Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. 3817-3825 - Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Convergence Properties of Stochastic Hypergradients. 3826-3834 - Tam Le, Truyen Nguyen:
Entropy Partial Transport with Tree Metrics: Theory and Practice. 3835-3843 - Ilker Demirel, Cem Tekin:
Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms. 3844-3852 - Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert:
Differentiable Divergences Between Time Series. 3853-3861 - Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, Sergios Theodoridis:
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning. 3862-3870 - Sheheryar Mehmood, Peter Ochs:
Differentiating the Value Function by using Convex Duality. 3871-3879 - Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Rate-Regularization and Generalization in Variational Autoencoders. 3880-3888 - Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco:
Asymptotics of Ridge(less) Regression under General Source Condition. 3889-3897 - Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki:
Longitudinal Variational Autoencoder. 3898-3906 - Matthew Hoffman, Alexey Radul, Pavel Sountsov:
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo. 3907-3915 - James Liley, Samuel R. Emerson, Bilal A. Mateen, Catalina A. Vallejos, Louis J. M. Aslett, Sebastian J. Vollmer:
Model updating after interventions paradoxically introduces bias. 3916-3924 - Yuyang Shi
On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models. 3925-3933 - Tam Le, Nhat Ho, Makoto Yamada:
Flow-based Alignment Approaches for Probability Measures in Different Spaces. 3934-3942 - Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi:
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. 3943-3951 - Burak Varici, Saurabh Sihag, Ali Tajer:
Learning Shared Subgraphs in Ising Model Pairs. 3952-3960 - Setareh Ariafar, Zelda Mariet, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek:
Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. 3961-3969 - Guang Zhao, Edward R. Dougherty, Byung-Jun Yoon, Francis J. Alexander, Xiaoning Qian:
Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty. 3970-3978 - Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith:
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. 3979-3987 - Mohammad Sadegh Talebi, Anders Jonsson, Odalric Maillard:
Improved Exploration in Factored Average-Reward MDPs. 3988-3996 - Steve Hanneke, Liu Yang:
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. 3997-4005 - Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco:
Regularized ERM on random subspaces. 4006-4014 - Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra:
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. 4015-4023 - Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. 4024-4032 - Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Hidden Cost of Randomized Smoothing. 4033-4041 - Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. 4042-4050 - Sooyong Jang, Insup Lee, James Weimer:
Improving Classifier Confidence using Lossy Label-Invariant Transformations. 4051-4059 - Rahi Kalantari, Mingyuan Zhou
Graph Gamma Process Linear Dynamical Systems. 4060-4068 - Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? 4069-4077 - Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. 4078-4086 - Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! 4087-4095
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