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23rd AISTATS 2020: Online [Palermo, Sicily, Italy]
- Silvia Chiappa, Roberto Calandra:
The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]. Proceedings of Machine Learning Research 108, PMLR 2020 - Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi:
Linearly Convergent Frank-Wolfe without Line-Search. 1-10 - Shinsaku Sakaue:
Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint. 11-21 - Shinsaku Sakaue:
On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness. 22-33 - Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. 34-44 - Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. 45-55 - Xuan Su, Wee Sun Lee, Zhen Zhang:
Multiplicative Gaussian Particle Filter. 56-65 - Jingyan Wang, Nihar B. Shah, R. Ravi:
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons. 66-76 - Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis:
Fast and Accurate Ranking Regression. 77-88 - Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. 89-99 - Guy Uziel, Ran El-Yaniv:
Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. 100-110 - Guy Uziel:
Nonparametric Sequential Prediction While Deep Learning the Kernel. 111-121 - Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei Li:
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation. 122-132 - Xiaorui Liu, Yao Li
, Jiliang Tang, Ming Yan:
A Double Residual Compression Algorithm for Efficient Distributed Learning. 133-143 - Alexander Terenin, Daniel Simpson, David Draper:
Asynchronous Gibbs Sampling. 144-154 - Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. 155-166 - Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Off-Policy Policy Gradient. 167-177 - Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel:
Non-Parametric Calibration for Classification. 178-190 - Geoffrey Wolfer, Aryeh Kontorovich:
Minimax Testing of Identity to a Reference Ergodic Markov Chain. 191-201 - Longfei Yan, W. Bastiaan Kleijn, Thushara D. Abhayapala:
A Linear-time Independence Criterion Based on a Finite Basis Approximation. 202-212 - Kevin Bello, Asish Ghoshal, Jean Honorio
Minimax Bounds for Structured Prediction Based on Factor Graphs. 213-222 - Bingcong Li, Meng Ma, Georgios B. Giannakis:
On the Convergence of SARAH and Beyond. 223-233 - Tim Pearce, Felix Leibfried, Alexandra Brintrup:
Uncertainty in Neural Networks: Approximately Bayesian Ensembling. 234-244 - Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi:
LIBRE: Learning Interpretable Boolean Rule Ensembles. 245-255 - Mehdi Molkaraie:
Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions. 256-265 - Melanie Weber:
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning. 266-276 - Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
Fair Decisions Despite Imperfect Predictions. 277-287 - Martin Mihelich, Charles Dognin, Yan Shu, Michael Blot:
A Characterization of Mean Squared Error for Estimator with Bagging. 288-297 - Yuexi Wang, Veronika Rocková:
Uncertainty Quantification for Sparse Deep Learning. 298-308 - Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. 309-319 - Wenkai Xu, Takeru Matsuda:
A Stein Goodness-of-fit Test for Directional Distributions. 320-330 - Taeeon Park, Taesup Moon:
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel. 331-340 - Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen:
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. 341-351 - Fengpei Li, Henry Lam, Siddharth Prusty:
Robust Importance Weighting for Covariate Shift. 352-362 - Peng Yang, Ping Li:
Adaptive Online Kernel Sampling for Vertex Classification. 363-373 - Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh:
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. 374-385 - Hideaki Ishibashi, Hideitsu Hino:
Stopping criterion for active learning based on deterministic generalization bounds. 386-397 - Zhaobin Kuang, Frederic Sala, Nimit Sharad Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Ré:
Ivy: Instrumental Variable Synthesis for Causal Inference. 398-410 - Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis:
High Dimensional Robust Sparse Regression. 411-421 - Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin:
Nested-Wasserstein Self-Imitation Learning for Sequence Generation. 422-433 - Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander:
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. 434-444 - Carolyn Kim, Mohsen Bayati:
Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling. 445-455 - Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou:
Fast Noise Removal for k-Means Clustering. 456-466 - Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang
Sketching Transformed Matrices with Applications to Natural Language Processing. 467-481 - Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan R. Curtin:
Unconditional Coresets for Regularized Loss Minimization. 482-492 - Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik:
ASAP: Architecture Search, Anneal and Prune. 493-503 - Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang:
Understanding Generalization in Deep Learning via Tensor Methods. 504-515 - Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machines. 516-526 - Xuhui Fan, Bin Li, Scott A. Sisson:
Online Binary Space Partitioning Forests. 527-537 - Benjamin Poignard, Makoto Yamada:
Sparse Hilbert-Schmidt Independence Criterion Regression. 538-548 - Wasim Huleihel, Ofer Shayevitz:
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model. 549-558 - Henrik Imberg, Johan Jonasson, Marina Axelson-Fisk:
Optimal sampling in unbiased active learning. 559-569 - Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. 570-579 - Christopher Tosh, Daniel Hsu:
Diameter-based Interactive Structure Discovery. 580-590 - Etienne Boursier, Vianney Perchet:
Utility/Privacy Trade-off through the lens of Optimal Transport. 591-601 - Maxime Laborde, Adam M. Oberman:
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. 602-612 - Chi-Ken Lu
Interpretable Deep Gaussian Processes with Moments. 613-623 - Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. 624-634 - Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Svetha Venkatesh, Laurence Park, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak:
Accelerated Bayesian Optimisation through Weight-Prior Tuning. 635-645 - Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir:
Variance Reduction for Evolution Strategies via Structured Control Variates. 646-656 - Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers:
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. 657-668 - Kenji Kawaguchi, Haihao Lu:
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. 669-679 - Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. 680-690 - Saptarshi Chakraborty
Entropy Weighted Power k-Means Clustering. 691-701 - Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. 702-712 - Yibo Zeng, Fei Feng, Wotao Yin:
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity. 713-723 - Dan Kushnir, Benjamin Mirabelli:
Active Community Detection with Maximal Expected Model Change. 724-734 - Takashi Nicholas Maeda, Shohei Shimizu:
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. 735-745 - Peng Zhao, Lijun Zhang, Yuan Jiang, Zhi-Hua Zhou:
A Simple Approach for Non-stationary Linear Bandits. 746-755 - Pedro Cisneros-Velarde, Alexander Petersen, Sang-Yun Oh
Distributionally Robust Formulation and Model Selection for the Graphical Lasso. 756-765 - Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu:
Efficient Spectrum-Revealing CUR Matrix Decomposition. 766-775 - Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. 776-787 - Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. 788-798 - Felix Dangel, Stefan Harmeling, Philipp Hennig:
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. 799-808 - Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
A Unified Statistically Efficient Estimation Framework for Unnormalized Models. 809-819 - Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. 820-830 - Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim:
Imputation estimators for unnormalized models with missing data. 831-841 - Youssef Mroueh:
Wasserstein Style Transfer. 842-852 - Kenji Kawaguchi, Leslie Pack Kaelbling:
Elimination of All Bad Local Minima in Deep Learning. 853-863 - Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi:
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. 864-874 - David McAllester, Karl Stratos:
Formal Limitations on the Measurement of Mutual Information. 875-884 - Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian:
Scalable Feature Selection for (Multitask) Gradient Boosted Trees. 885-894 - Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera
Model-Agnostic Counterfactual Explanations for Consequential Decisions. 895-905 - Hsiang Hsu, Shahab Asoodeh, Flávio P. Calmon:
Obfuscation via Information Density Estimation. 906-917 - Chloe Ching-Yun Hsu, Michaela Hardt, Moritz Hardt:
Linear Dynamics: Clustering without identification. 918-929 - Kelly Geyer, Anastasios Kyrillidis, Amir Kalev:
Low-rank regularization and solution uniqueness in over-parameterized matrix sensing. 930-940 - Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri:
Robustness for Non-Parametric Classification: A Generic Attack and Defense. 941-951 - Shiyun Chen, Shiva Prasad Kasiviswanathan:
Contextual Online False Discovery Rate Control. 952-961 - Tom Hess, Sivan Sabato:
Sequential no-Substitution k-Median-Clustering. 962-972 - Sanjoy Dasgupta, Sivan Sabato:
Robust Learning from Discriminative Feature Feedback. 973-982 - Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti:
Hermitian matrices for clustering directed graphs: insights and applications. i - Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. 993-1004 - Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. 1005-1014 - Xingchen Ma
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. 1015-1025 - Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney:
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. 1026-1035 - Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka:
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. 1036-1046 - Zhize Li, Jian Li:
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization. 1047-1057 - Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi:
Black Box Submodular Maximization: Discrete and Continuous Settings. 1058-1070 - Ilija Bogunovic, Andreas Krause, Jonathan Scarlett:
Corruption-Tolerant Gaussian Process Bandit Optimization. 1071-1081 - Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. 1082-1092 - Avishek Ghosh, Kannan Ramchandran:
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression. 1093-1103 - Anamay Chaturvedi, Jonathan Scarlett:
Learning Gaussian Graphical Models via Multiplicative Weights. 1104-1114 - Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. 1115-1125 - Stefano Peluchetti, Stefano Favaro:
Infinitely deep neural networks as diffusion processes. 1126-1136 - Stefano Peluchetti, Stefano Favaro, Sandra Fortini:
Stable behaviour of infinitely wide deep neural networks. 1137-1146 - Xinyi Wang, Yi Yang:
Neural Topic Model with Attention for Supervised Learning. 1147-1156 - Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. 1157-1167 - Leonardo Cella, Nicolò Cesa-Bianchi:
Stochastic Bandits with Delay-Dependent Payoffs. 1168-1177 - Fabien Lauer:
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses. 1178-1187 - Matteo Papini, Andrea Battistello, Marcello Restelli:
Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration. 1188-1199 - Jan Stuehmer, Richard E. Turner, Sebastian Nowozin:
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. 1200-1210 - Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet:
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players. 1211-1221 - François-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi:
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport. 1222-1232 - Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. 1233-1243 - Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki:
Simulator Calibration under Covariate Shift with Kernels. 1244-1253 - Milan Vojnovic, Se-Young Yun, Kaifang Zhou:
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models. 1254-1264 - Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup:
A Locally Adaptive Bayesian Cubature Method. 1265-1275 - Klim Efremenko
Fast and Bayes-consistent nearest neighbors. 1276-1286 - Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. 1287-1296 - Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. 1297-1307 - Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang:
Deep Active Learning: Unified and Principled Method for Query and Training. 1308-1318 - Botao Hao, Anru R. Zhang, Guang Cheng:
Sparse and Low-rank Tensor Estimation via Cubic Sketchings. 1319-1330 - Arnak S. Dalalyan, Nicolas Schreuder, Victor-Emmanuel Brunel:
A nonasymptotic law of iterated logarithm for general M-estimators. 1331-1341 - Thomas Nedelec, Clément Calauzènes, Vianney Perchet, Noureddine El Karoui:
Robust Stackelberg buyers in repeated auctions. 1342-1351 - Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. 1352-1362 - Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang:
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. 1363-1374 - Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien:
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. 1375-1386 - Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. 1387-1398 - Prathamesh Mayekar, Himanshu Tyagi:
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization. 1399-1409 - Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis:
Rep the Set: Neural Networks for Learning Set Representations. 1410-1420 - Robin Vogel, Stéphan Clémençon:
A Multiclass Classification Approach to Label Ranking. 1421-1430 - Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. 1431-1441 - Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman:
A principled approach for generating adversarial images under non-smooth dissimilarity metrics. 1442-1452 - Weizhi Li, Gautam Dasarathy, Visar Berisha:
Regularization via Structural Label Smoothing. 1453-1463 - Jiacheng Zhuo, Qi Lei, Alex Dimakis, Constantine Caramanis:
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls. 1464-1474 - Yuege Xie, Xiaoxia Wu, Rachel A. Ward:
Linear Convergence of Adaptive Stochastic Gradient Descent. 1475-1485 - Andi Nika, Sepehr Elahi
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization. 1486-1496 - Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil:
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. 1497-1507 - Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou:
Bandit Convex Optimization in Non-stationary Environments. 1508-1518 - Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. 1519-1528 - Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman:
Bayesian Image Classification with Deep Convolutional Gaussian Processes. 1529-1539 - Jonathan Lorraine, Paul Vicol, David Duvenaud:
Optimizing Millions of Hyperparameters by Implicit Differentiation. 1540-1552 - Rickard Brüel Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba:
A Topology Layer for Machine Learning. 1553-1563 - Rishit Sheth, Nicoló Fusi:
Differentiable Feature Selection by Discrete Relaxation. 1564-1572 - James Bell, Aurélien Bellet, Adrià Gascón, Tejas Kulkarni:
Private Protocols for U-Statistics in the Local Model and Beyond. 1573-1583 - Sheheryar Mehmood, Peter Ochs:
Automatic Differentiation of Some First-Order Methods in Parametric Optimization. 1584-1594 - Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam:
DYNOTEARS: Structure Learning from Time-Series Data. 1595-1605 - David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. 1606-1617 - Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. 1618-1628 - Hossein Valavi, Sulin Liu, Peter J. Ramadge:
Revisiting the Landscape of Matrix Factorization. 1629-1638 - David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. 1639-1650 - Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt:
GP-VAE: Deep Probabilistic Time Series Imputation. 1651-1661 - Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi:
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction. 1662-1672 - Dennis Wei, Karthikeyan Natesan Ramamurthy, Flávio P. Calmon:
Optimized Score Transformation for Fair Classification. 1673-1683 - He Zhao, Piyush Rai, Lan Du, Wray L. Buntine
Variational Autoencoders for Sparse and Overdispersed Discrete Data. 1684-1694 - Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Spatio-temporal alignments: Optimal transport through space and time. 1695-1704 - Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
Accelerating Smooth Games by Manipulating Spectral Shapes. 1705-1715 - Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. 1716-1726 - Jeongyeol Kwon, Constantine Caramanis:
EM Converges for a Mixture of Many Linear Regressions. 1727-1736 - Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta:
Locally Accelerated Conditional Gradients. 1737-1747 - Andrew Warrington, Frank Wood, Saeid Naderiparizi:
Coping With Simulators That Don't Always Return. 1748-1758 - Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. 1759-1769 - Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Equalized odds postprocessing under imperfect group information. 1770-1780 - Julian Katz-Samuels, Kevin G. Jamieson:
The True Sample Complexity of Identifying Good Arms. 1781-1791 - Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Validated Variational Inference via Practical Posterior Error Bounds. 1792-1802 - Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. 1803-1812 - Armin Askari, Alexandre d'Aspremont, Laurent El Ghaoui:
Naive Feature Selection: Sparsity in Naive Bayes. 1813-1822 - Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko:
Fixed-confidence guarantees for Bayesian best-arm identification. 1823-1832 - Hussein Hazimeh, Rahul Mazumder:
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. 1833-1843 - Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. 1844-1854 - Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son:
Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning. 1855-1865 - Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. 1866-1876 - Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, Changyou Chen:
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory. 1877-1887 - Yi Ding, Panos Toulis:
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods. 1888-1898 - Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite:
RelatIF: Identifying Explanatory Training Samples via Relative Influence. 1899-1909 - Qin Lu, Georgios Vasileios Karanikolas, Yanning Shen, Georgios B. Giannakis:
Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability. 1910-1920 - Thanh Tang Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh:
Distributionally Robust Bayesian Quadrature Optimization. 1921-1931 - Jiaxin Shi, Michalis K. Titsias, Andriy Mnih:
Sparse Orthogonal Variational Inference for Gaussian Processes. 1932-1942 - Yu Wang
The Sylvester Graphical Lasso (SyGlasso). 1943-1953 - Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. 1954-1964 - Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. 1965-1976 - Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
Discrete Action On-Policy Learning with Action-Value Critic. 1977-1987 - Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton:
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. 1988-1998 - Vidit Saxena, Joakim Jaldén, Joseph Gonzalez:
Thompson Sampling for Linearly Constrained Bandits. 1999-2009 - Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh:
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. 2010-2020 - Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani:
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. 2021-2031 - Yang Liu, David P. Helmbold:
Online Learning Using Only Peer Prediction. 2032-2042 - Serena Lutong Wang, Maya R. Gupta:
Deontological Ethics By Monotonicity Shape Constraints. 2043-2054 - Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida:
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. 2055-2065 - Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier:
Randomized Exploration in Generalized Linear Bandits. 2066-2076 - Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher:
Assessing Local Generalization Capability in Deep Models. 2077-2087 - Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. 2088-2097 - Christoph Zimmer, Danny Driess, Mona Meister, Duy Nguyen-Tuong:
Adaptive Discretization for Evaluation of Probabilistic Cost Functions. 2098-2108 - Alexander Hanbo Li, Jelena Bradic:
Censored Quantile Regression Forest. 2109-2119 - Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi:
Choosing the Sample with Lowest Loss makes SGD Robust. 2120-2130 - Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty:
Learning with minibatch Wasserstein : asymptotic and gradient properties. 2131-2141 - Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. 2142-2152 - Dongha Kim, Jaesung Hwang, Yongdai Kim:
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models. 2153-2163 - Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan:
Conditional Linear Regression. 2164-2173 - Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause:
Distributionally Robust Bayesian Optimization. 2174-2184 - Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
On the optimality of kernels for high-dimensional clustering. 2185-2195 - Dan Garber, Ben Kretzu:
Improved Regret Bounds for Projection-free Bandit Convex Optimization. 2196-2206 - Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen:
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. 2207-2217 - Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots:
Online Learning with Continuous Variations: Dynamic Regret and Reductions. 2218-2228 - Shinji Ito:
An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss. 2229-2239 - Marco Podda, Davide Bacciu, Alessio Micheli:
A Deep Generative Model for Fragment-Based Molecule Generation. 2240-2250 - Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen:
Deep Structured Mixtures of Gaussian Processes. 2251-2261 - Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska, Christian Daniel, Melanie N. Zeilinger:
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. 2262-2272 - David G. Harris, Thomas W. Pensyl, Aravind Srinivasan, Khoa Trinh:
Dependent randomized rounding for clustering and partition systems with knapsack constraints. 2273-2283 - Ondrej Kuzelka, Yuyi Wang:
Domain-Liftability of Relational Marginal Polytopes. 2284-2292 - Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko:
Derivative-Free & Order-Robust Optimisation. 2293-2303 - Yao Zhang, Daniel Jarrett, Mihaela van der Schaar:
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning. 2304-2314 - Seppo Virtanen, Mark Girolami:
Dynamic content based ranking. 2315-2324 - Riccardo Fogliato, Alexandra Chouldechova, Max G'Sell:
Fairness Evaluation in Presence of Biased Noisy Labels. 2325-2336 - Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. 2337-2347 - Giovanni Neglia, Chuan Xu, Don Towsley, Gianmarco Calbi:
Decentralized gradient methods: does topology matter? 2348-2358 - Giorgia Ramponi, Amarildo Likmeta
Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions. 2359-2369 - Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. 2370-2380 - Jinming Xu, Ye Tian, Ying Sun, Gesualdo Scutari:
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks. 2381-2391 - Weiqiang Wu, Jing Yang, Cong Shen:
Stochastic Linear Contextual Bandits with Diverse Contexts. 2392-2401 - Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana:
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. 2402-2412 - Arjun Sondhi, David Arbour, Drew Dimmery:
Balanced Off-Policy Evaluation in General Action Spaces. 2413-2423 - William T. Stephenson, Tamara Broderick:
Approximate Cross-Validation in High Dimensions with Guarantees. 2424-2434 - Evani Radiya-Dixit, Xin Wang:
How fine can fine-tuning be? Learning efficient language models. 2435-2443 - Danqing Pan, Tong Wang, Satoshi Hara:
Interpretable Companions for Black-Box Models. 2444-2454 - Yue Qin, Jian Peng, Yuan Zhou:
A PTAS for the Bayesian Thresholding Bandit Problem. 2455-2464 - Antti Koskela, Antti Honkela:
Learning Rate Adaptation for Differentially Private Learning. 2465-2475 - Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk:
Thresholding Graph Bandits with GrAPL. 2476-2485 - David Janz, David R. Burt, Javier Gonzalez:
Bandit optimisation of functions in the Matérn kernel RKHS. 2486-2495 - Borja Balle, Gilles Barthe, Marco Gaboardi
Hypothesis Testing Interpretations and Renyi Differential Privacy. 2496-2506 - Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik:
Lipschitz Continuous Autoencoders in Application to Anomaly Detection. 2507-2517 - Moshe Shechner, Or Sheffet, Uri Stemmer:
Private k-Means Clustering with Stability Assumptions. 2518-2528 - Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. 2529-2538 - Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother, Florian Jug, Paul Swoboda, Bogdan Savchynskyy:
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. 2539-2549 - Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Precision-Recall Curves Using Information Divergence Frontiers. 2550-2559 - Antti Koskela, Joonas Jälkö, Antti Honkela:
Computing Tight Differential Privacy Guarantees Using FFT. 2560-2569 - Gian Maria Marconi, Carlo Ciliberto, Lorenzo Rosasco:
Hyperbolic Manifold Regression. 2570-2580 - Charlie Frogner, Tomaso A. Poggio:
Approximate Inference with Wasserstein Gradient Flows. 2581-2590 - Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski:
Thresholding Bandit Problem with Both Duels and Pulls. 2591-2600 - Jose Gallego-Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien:
GAIT: A Geometric Approach to Information Theory. 2601-2611 - James A. Grant, David S. Leslie:
On Thompson Sampling for Smoother-than-Lipschitz Bandits. 2612-2622 - Rianne de Heide, Alisa Kirichenko, Peter Grunwald, Nishant A. Mehta:
Safe-Bayesian Generalized Linear Regression. 2623-2633 - Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác:
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. 2634-2644 - Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. 2645-2654 - Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon:
Support recovery and sup-norm convergence rates for sparse pivotal estimation. 2655-2665 - Hui Yuan, Yingyu Liang:
Learning Entangled Single-Sample Distributions via Iterative Trimming. 2666-2676 - Travis Moore, Weng-Keen Wong:
The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time. 2677-2686 - Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney:
Statistical guarantees for local graph clustering. 2687-2697 - Yuhao Wang, Uma Roy, Caroline Uhler:
Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters. 2698-2708 - Alvaro Henrique Chaim Correia
On Pruning for Score-Based Bayesian Network Structure Learning. 2709-2718 - Quentin Berthet, Nicolai Baldin:
Statistical and Computational Rates in Graph Logistic Regression. 2719-2730 - Poompol Buathong
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. 2731-2741 - Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich:
Rk-means: Fast Clustering for Relational Data. 2742-2752 - Loucas Pillaud-Vivien, Francis R. Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz:
Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. 2753-2763 - Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig:
Integrals over Gaussians under Linear Domain Constraints. 2764-2774 - Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. 2775-2785 - Mathieu Carrière, Frédéric Chazal, Yuichi Ike
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. 2786-2796 - Insu Han, Jennifer Gillenwater:
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. 2797-2807 - Xubo Yue, Raed Al Kontar:
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout. 2808-2818 - Borislav Ikonomov, Michael U. Gutmann:
Robust Optimisation Monte Carlo. 2819-2829 - Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. 2830-2840 - Steve Huntsman:
Fast Markov chain Monte Carlo algorithms via Lie groups. 2841-2851 - Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. 2852-2862 - Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. 2863-2873 - Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman:
Doubly Sparse Variational Gaussian Processes. 2874-2884 - Victor Valls, George Iosifidis, Douglas J. Leith, Leandros Tassiulas:
Online Convex Optimization with Perturbed Constraints: Optimal Rates against Stronger Benchmarks. 2885-2895 - Qi Zhao, Ze Ye, Chao Chen, Yusu Wang:
Persistence Enhanced Graph Neural Network. 2896-2906 - Dominik Janzing, Lenon Minorics, Patrick Blöbaum:
Feature relevance quantification in explainable AI: A causal problem. 2907-2916 - Kaspar Märtens, Christopher Yau:
Neural Decomposition: Functional ANOVA with Variational Autoencoders. 2917-2927 - Kaspar Märtens, Christopher Yau:
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders. 2928-2937 - Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov:
How To Backdoor Federated Learning. 2938-2948 - Brian Lucena:
Exploiting Categorical Structure Using Tree-Based Methods. 2949-2958 - Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. 2959-2969 - Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause:
Mixed Strategies for Robust Optimization of Unknown Objectives. 2970-2980 - Atsushi Nitanda, Taiji Suzuki:
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. 2981-2991 - Aaron Sidford, Mengdi Wang, Lin Yang
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. 2992-3002 - Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. 3003-3014 - Gang Wang, Georgios B. Giannakis:
Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning. 3015-3024 - Théo Galy-Fajou, Florian Wenzel, Manfred Opper:
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. 3025-3035 - Divya Grover, Debabrota Basu, Christos Dimitrakakis:
Bayesian Reinforcement Learning via Deep, Sparse Sampling. 3036-3045 - Daniil Polykovskiy, Dmitry P. Vetrov:
Deterministic Decoding for Discrete Data in Variational Autoencoders. 3046-3056 - Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Monotonic Gaussian Process Flows. 3057-3067 - Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern:
Flexible distribution-free conditional predictive bands using density estimators. 3068-3077 - Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Structured Embeddings. 3078-3087 - Wil O. C. Ward, Tom Ryder, Dennis Prangle, Mauricio A. Álvarez:
Black-Box Inference for Non-Linear Latent Force Models. 3088-3098 - Anant Raj, Cameron Musco, Lester Mackey:
Importance Sampling via Local Sensitivity. 3099-3109 - Mojmir Mutny, Michal Derezinski, Andreas Krause:
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling. 3110-3120 - Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian:
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection. 3121-3132 - Kaige Yang, Laura Toni, Xiaowen Dong:
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis. 3133-3143 - Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas:
Enriched mixtures of generalised Gaussian process experts. 3144-3154 - Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González:
Causal Bayesian Optimization. 3155-3164 - Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux:
Linear predictor on linearly-generated data with missing values: non consistency and solutions. 3165-3174 - Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli:
A Novel Confidence-Based Algorithm for Structured Bandits. 3175-3185 - Quentin Mérigot, Alex Delalande
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space. 3186-3196 - Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Bayesian experimental design using regularized determinantal point processes. 3197-3207 - Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh:
Non-exchangeable feature allocation models with sublinear growth of the feature sizes. 3208-3218 - Sangdon Park, Osbert Bastani, James Weimer, Insup Lee:
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation. 3219-3229 - Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao:
Inference of Dynamic Graph Changes for Functional Connectome. 3230-3240 - Ziqiao Ao, Jinglai Li
An approximate KLD based experimental design for models with intractable likelihoods. 3241-3251 - M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. 3252-3262 - Grigory Yaroslavtsev, Samson Zhou, Dmitrii Avdiukhin:
"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack. 3263-3274 - Rakshith Sharma Srinivasa, Mark A. Davenport, Justin Romberg:
Sample complexity bounds for localized sketching. 3275-3284 - Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays. 3285-3294 - Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Mihaela van der Schaar, Jem Rashbass:
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. 3295-3305 - Beheshteh T. Rakhshan, Guillaume Rabusseau:
Tensorized Random Projections. 3306-3316 - Heejong Bong, Wanshan Li, Shamindra Shrotriya, Alessandro Rinaldo:
Nonparametric Estimation in the Dynamic Bradley-Terry Model. 3317-3326 - Ziv Goldfeld, Kristjan H. Greenewald:
Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency. 3327-3337 - Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath:
Learning in Gated Neural Networks. 3338-3348 - Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin:
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. 3349-3361 - Fangda Gu, Armin Askari, Laurent El Ghaoui:
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training. 3362-3371 - Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen J. Roberts:
Adversarial Robustness Guarantees for Classification with Gaussian Processes. 3372-3382 - Yue Wang, Linbo Wang:
Causal inference in degenerate systems: An impossibility result. 3383-3392 - Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. 3393-3403 - Hisham Husain, Borja Balle, Zac Cranko, Richard Nock:
Local Differential Privacy for Sampling. 3404-3413 - Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. 3414-3425 - Venkatesh Saligrama, Alexander Olshevsky, Julien M. Hendrickx:
Minimax Rank-$1$ Matrix Factorization. 3426-3436 - Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. 3437-3449 - Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow
Data Generation for Neural Programming by Example. 3450-3459 - Yunfeng Cai, Ping Li:
An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem. 3460-3470 - Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai:
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits. 3471-3481 - Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh:
Understanding the Effects of Batching in Online Active Learning. 3482-3492 - Côme Fiegel, Victor Gabillon, Michal Valko:
Adaptive multi-fidelity optimization with fast learning rates. 3493-3502 - Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux:
On the interplay between noise and curvature and its effect on optimization and generalization. 3503-3513 - Ching-An Cheng, Remi Tachet des Combes, Byron Boots, Geoffrey J. Gordon:
A Reduction from Reinforcement Learning to No-Regret Online Learning. 3514-3524 - Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk:
The Implicit Regularization of Ordinary Least Squares Ensembles. 3525-3535 - Botao Hao, Tor Lattimore, Csaba Szepesvári:
Adaptive Exploration in Linear Contextual Bandit. 3536-3545 - Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta:
A Three Sample Hypothesis Test for Evaluating Generative Models. 3546-3556 - Surbhi Goel:
Learning Ising and Potts Models with Latent Variables. 3557-3566 - Dravyansh Sharma, Maria-Florina Balcan, Travis Dick:
Learning piecewise Lipschitz functions in changing environments. 3567-3577 - Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez:
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. 3578-3588 - Nikitas Rontsis, Paul Goulart:
Optimal Approximation of Doubly Stochastic Matrices. 3589-3598 - Zhifeng Kong, Kamalika Chaudhuri:
The Expressive Power of a Class of Normalizing Flow Models. 3599-3609 - Grégoire Mialon, Julien Mairal, Alexandre d'Aspremont:
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions. 3610-3620 - Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba:
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. 3621-3631 - Yiming Yan, Melissa Ailem, Fei Sha:
Amortized Inference of Variational Bounds for Learning Noisy-OR. 3632-3641 - Nicholas Sterge, Bharath K. Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi:
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. 3642-3652 - Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas:
Logistic regression with peer-group effects via inference in higher-order Ising models. 3653-3663 - Cynthia Rush:
An Asymptotic Rate for the LASSO Loss. 3664-3673 - Grzegorz Gluch, Rüdiger L. Urbanke:
Constructing a provably adversarially-robust classifier from a high accuracy one. 3674-3684 - Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson:
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. 3685-3695 - Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi:
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free. 3696-3706 - Pon Kumar Sharoff, Nishant A. Mehta, Ravi Ganti:
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option. 3707-3716 - Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier, Michael Mitzenmacher:
Prophets, Secretaries, and Maximizing the Probability of Choosing the Best. 3717-3727 - Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang:
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models. 3728-3738 - Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis:
Sharp Asymptotics and Optimal Performance for Inference in Binary Models. 3739-3749 - Mingzhang Yin, Y. X. Rachel Wang, Purnamrita Sarkar:
A Theoretical Case Study of Structured Variational Inference for Community Detection. 3750-3761 - Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li:
Orthogonal Gradient Descent for Continual Learning. 3762-3773 - Dan Piponi, Matthew D. Hoffman, Pavel Sountsov:
Hamiltonian Monte Carlo Swindles. 3774-3783 - Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko:
A single algorithm for both restless and rested rotting bandits. 3784-3794 - Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. 3795-3805 - Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas, Michael I. Jordan:
The Power of Batching in Multiple Hypothesis Testing. 3806-3815 - Emilio Rafael Balda, Niklas Koep, Arash Behboodi, Rudolf Mathar:
Adversarial Risk Bounds through Sparsity based Compression. 3816-3825 - Zheyang Shen, Markus Heinonen, Samuel Kaski:
Learning spectrograms with convolutional spectral kernels. 3826-3836 - Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li:
Federated Heavy Hitters Discovery with Differential Privacy. 3837-3847 - Chi-Hua Wang, Guang Cheng:
Online Batch Decision-Making with High-Dimensional Covariates. 3848-3857 - Osbert Bastani:
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems. 3858-3869 - Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud:
Scalable Gradients for Stochastic Differential Equations. 3870-3882 - Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans:
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. 3883-3893 - Zepeng Huo, Arash Pakbin, Xiaohan Chen, Nathan C. Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi:
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery. 3894-3904 - Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou
Learnable Bernoulli Dropout for Bayesian Deep Learning. 3905-3916 - Eli Sherman, David Arbour, Ilya Shpitser:
General Identification of Dynamic Treatment Regimes Under Interference. 3917-3927 - Samory Kpotufe, Bharath K. Sriperumbudur:
Gaussian Sketching yields a J-L Lemma in RKHS. 3928-3937 - Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. 3938-3947 - Ming Yin, Yu-Xiang Wang:
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. 3948-3958 - Zhengjue Wang, Chaojie Wang, Hao Zhang, Zhibin Duan, Mingyuan Zhou
Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification. 3959-3969 - Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. 3970-3979 - Dongruo Zhou
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. 3980-3990 - Yanshuai Cao, Peng Xu:
Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer. 3991-4001 - Bryan Andrews:
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge. 4002-4011 - Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi:
One Sample Stochastic Frank-Wolfe. 4012-4023 - Tolga Ergen, Mert Pilanci:
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models. 4024-4033 - Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
A Robust Univariate Mean Estimator is All You Need. 4034-4044 - Li-Fang Cheng, Bianca Dumitrascu, Michael Minyi Zhang, Corey Chivers
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes. 4045-4055 - Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton:
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. 4056-4066 - Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki:
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. 4067-4077 - Robert John Durrant, Nick Jin Sean Lim:
A Diversity-aware Model for Majority Vote Ensemble Accuracy. 4078-4087 - Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing. 4088-4097 - Daniel Irving Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler:
Ordering-Based Causal Structure Learning in the Presence of Latent Variables. 4098-4108 - Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama:
Budget Learning via Bracketing. 4109-4119 - Po-An Wang, Alexandre Proutière, Kaito Ariu, Yassir Jedra
Optimal Algorithms for Multiplayer Multi-Armed Bandits. 4120-4129 - Rizal Fathony, J. Zico Kolter:
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. 4130-4140 - Praneeth Kacham, David P. Woodruff:
Optimal Deterministic Coresets for Ridge Regression. 4141-4150 - Sandesh Adhikary, Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots:
Expressiveness and Learning of Hidden Quantum Markov Models. 4151-4161 - Yunfeng Cai, Ping Li:
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations. 4162-4172 - Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn:
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation. 4173-4183 - Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron C. Courville:
Stochastic Neural Network with Kronecker Flow. 4184-4194 - Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Fair Correlation Clustering. 4195-4205 - Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati:
Towards Competitive N-gram Smoothing. 4206-4215 - Gi-Bum Kim, Seyoung Kim:
Multi-level Gaussian Graphical Models Conditional on Covariates. 4216-4225 - Christian Carmona, Geoff K. Nicholls:
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components. 4226-4235 - Hadi Mohaghegh Dolatabadi, Sarah M. Erfani, Christopher Leckie:
Invertible Generative Modeling using Linear Rational Splines. 4236-4246 - Maryam Majzoubi, Anna Choromanska:
LdSM: Logarithm-depth Streaming Multi-label Decision Trees. 4247-4257 - Moontae Lee, David Bindel, David Mimno:
Prior-aware Composition Inference for Spectral Topic Models. 4258-4268 - Molei Tao, Tomoki Ohsawa:
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems. 4269-4280 - Aadirupa Saha, Aditya Gopalan:
Best-item Learning in Random Utility Models with Subset Choices. 4281-4291 - Abhishek Kumar, Ben Poole, Kevin Murphy:
Regularized Autoencoders via Relaxed Injective Probability Flow. 4292-4301 - Cheolmin Kim, Diego Klabjan:
Stochastic Variance-Reduced Algorithms for PCA with Arbitrary Mini-Batch Sizes. 4302-4312 - Mingchen Li, Mahdi Soltanolkotabi
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. 4313-4324 - Zhimeng Pan, Zheng Wang, Shandian Zhe:
Scalable Nonparametric Factorization for High-Order Interaction Events. 4325-4335 - Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. 4336-4345 - Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb:
Adaptive, Distribution-Free Prediction Intervals for Deep Networks. 4346-4356 - Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. 4357-4366 - Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff:
Automatic Differentiation of Sketched Regression. 4367-4376 - Weihao Kong, Emma Brunskill, Gregory Valiant:
Sublinear Optimal Policy Value Estimation in Contextual Bandits. 4377-4387 - Semih Cayci, Atilla Eryilmaz, R. Srikant:
Budget-Constrained Bandits over General Cost and Reward Distributions. 4388-4398 - Mohsen Ferdosi, Arash Gholami Davoodi, Hosein Mohimani:
Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity. 4399-4409 - Omid Sadeghi
Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints. 4410-4419 - Jason Ren, Russell Kunes, Finale Doshi-Velez:
Prediction Focused Topic Models via Feature Selection. 4420-4429 - Dongruo Zhou
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. 4430-4440 - Christian Weilbach, Boyan Beronov
Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models. 4441-4451 - Yu Jin, Andreas Loukas, Joseph F. JáJá:
Graph Coarsening with Preserved Spectral Properties. 4452-4462 - Andrew Ilyas, Emmanouil Zampetakis
A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. 4463-4473 - Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. 4474-4484 - Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang:
Finite-Time Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation. 4485-4495 - Raul Astudillo, Peter I. Frazier
Multi-attribute Bayesian optimization with interactive preference learning. 4496-4507 - Ishaq Aden-Ali, Hassan Ashtiani:
On the Sample Complexity of Learning Sum-Product Networks. 4508-4518 - Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik:
Tighter Theory for Local SGD on Identical and Heterogeneous Data. 4519-4529 - Ashia C. Wilson, Maximilian Kasy, Lester Mackey:
Approximate Cross-validation: Guarantees for Model Assessment and Selection. 4530-4540 - Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu:
On Minimax Optimality of GANs for Robust Mean Estimation. 4541-4551 - Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Auditing ML Models for Individual Bias and Unfairness. 4552-4562 - Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu:
Stein Variational Inference for Discrete Distributions. 4563-4572 - Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. 4573-4582 - Shengjia Zhao, Christopher Yeh, Stefano Ermon:
A Framework for Sample Efficient Interval Estimation with Control Variates. 4583-4592 - Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low:
Nonmyopic Gaussian Process Optimization with Macro-Actions. 4593-4604
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