default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID25th AISTATS 2022: Virtual Event
- Gustau Camps-Valls, Francisco J. R. Ruiz, Isabel Valera
:
International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event. Proceedings of Machine Learning Research 151, PMLR 2022 - Andrew Silva, Rohit Chopra, Matthew C. Gombolay:
Cross-Loss Influence Functions to Explain Deep Network Representations. 1-17 - Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang:
Federated Reinforcement Learning with Environment Heterogeneity. 18-37 - Lan V. Truong:
On Linear Model with Markov Signal Priors. 38-53 - Jie Bian, Kwang-Sung Jun:
Maillard Sampling: Boltzmann Exploration Done Optimally. 54-72 - Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun:
Norm-Agnostic Linear Bandits. 73-91 - Zihan Li, Jonathan Scarlett:
Gaussian Process Bandit Optimization with Few Batches. 92-107 - Tavor Z. Baharav, Gary Cheng, Mert Pilanci, David Tse:
Approximate Function Evaluation via Multi-Armed Bandits. 108-135 - Yue Xing, Qifan Song, Guang Cheng:
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. 136-168 - Hyun-Suk Lee:
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy. 169-187 - Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy:
Deep Layer-wise Networks Have Closed-Form Weights. 188-225 - Oliver Cobb, Arnaud Van Looveren, Janis Klaise:
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates. 226-239 - Parnian Kassraie, Andreas Krause:
Neural Contextual Bandits without Regret. 240-278 - Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower:
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. 279-318 - Baturalp Yalcin, Haixiang Zhang, Javad Lavaei, Somayeh Sojoudi:
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods. 319-341 - Samrat Mukhopadhyay, Sourav Sahoo, Abhishek Sinha:
k-experts - Online Policies and Fundamental Limits. 342-365 - Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel:
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. 366-402 - Suho Shin, Seungjoon Lee, Jungseul Ok:
Multi-armed Bandit Algorithm against Strategic Replication. 403-431 - Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du:
Gap-Dependent Bounds for Two-Player Markov Games. 432-455 - Aadirupa Saha, Suprovat Ghoshal:
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. 456-482 - Zenan Ling, Fan Zhou, Meng Wei, Quanshi Zhang:
Exploring Image Regions Not Well Encoded by an INN. 483-509 - Xupeng Shi, Pengfei Zheng, A. Adam Ding, Yuan Gao, Weizhong Zhang:
Finding Dynamics Preserving Adversarial Winning Tickets. 510-528 - Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. 529-545 - Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes:
Jointly Efficient and Optimal Algorithms for Logistic Bandits. 546-580 - Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing:
Obtaining Causal Information by Merging Datasets with MAXENT. 581-603 - Hengchao Chen, Qiang Sun:
Distributed Sparse Multicategory Discriminant Analysis. 604-624 - Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. 625-639 - Kevin Bello, Chuyang Ke, Jean Honorio:
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. 640-654 - Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko:
Marginalized Operators for Off-policy Reinforcement Learning. 655-679 - Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik:
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. 680-720 - Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud:
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. 721-738 - Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour:
Causally motivated shortcut removal using auxiliary labels. 739-766 - Anirban Santara, Gaurav Aggarwal, Shuai Li, Claudio Gentile:
Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback. 767-797 - Sela Fried, Geoffrey Wolfer:
Identity Testing of Reversible Markov Chains. 798-817 - Or Dinari, Oren Freifeld:
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data. 818-835 - Youssef Diouane, Aurélien Lucchi, Vihang Prakash Patil:
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. 836-859 - Gábor Balázs:
Adaptively Partitioning Max-Affine Estimators for Convex Regression. 860-874 - Jinlin Lai, Justin Domke, Daniel Sheldon:
Variational Marginal Particle Filters. 875-895 - Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. 896-921 - Amanda Olmin, Fredrik Lindsten:
Robustness and Reliability When Training With Noisy Labels. 922-942 - Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol:
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. 943-970 - Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach:
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. 971-990 - Anas Barakat, Pascal Bianchi, Julien Lehmann:
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. 991-1040 - Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha:
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. 1041-1061 - Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis:
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. 1062-1077 - Jiaxin Hu, Miaoyan Wang:
Multiway Spherical Clustering via Degree-Corrected Tensor Block Models. 1078-1119 - Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada:
Fixed Support Tree-Sliced Wasserstein Barycenter. 1120-1137 - Jean Ruppert, Marharyta Aleksandrova, Thomas Engel:
k-Pareto Optimality-Based Sorting with Maximization of Choice. 1138-1160 - Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao:
Towards Return Parity in Markov Decision Processes. 1161-1178 - Vivek F. Farias, Andrew A. Li, Tianyi Peng:
Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise. 1179-1189 - David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic:
Survival regression with proper scoring rules and monotonic neural networks. 1190-1205 - Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Physics Informed Deep Kernel Learning. 1206-1218 - Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. 1219-1250 - Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
Heavy-tailed Streaming Statistical Estimation. 1251-1282 - Agnieszka Slowik, Léon Bottou:
On Distributionally Robust Optimization and Data Rebalancing. 1283-1297 - Elad Romanov, Or Ordentlich:
Spiked Covariance Estimation from Modulo-Reduced Measurements. 1298-1320 - Pedro Cisneros-Velarde, Francesco Bullo:
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows. 1321-1335 - Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang:
Robust Probabilistic Time Series Forecasting. 1336-1358 - Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak J. Mortazavi, Shuai Huang, Xiaoning Qian:
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition. 1359-1379 - Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-values. 1380-1402 - Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
A Witness Two-Sample Test. 1403-1419 - Graham Cormode, Akash Bharadwaj:
Sample-and-threshold differential privacy: Histograms and applications. 1420-1431 - Vlad Winter, Or Dinari, Oren Freifeld:
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution. 1432-1456 - Jan MacDonald, Stephan Wäldchen:
A Complete Characterisation of ReLU-Invariant Distributions. 1457-1484 - Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. 1485-1502 - Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. 1503-1526 - Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson, Alexander G. Schwing:
Equivariance Discovery by Learned Parameter-Sharing. 1527-1545 - Youming Tao, Yulian Wu, Peng Zhao, Di Wang:
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. 1546-1574 - Wenkai Xu:
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit. 1575-1597 - Cecilia Ferrando, Shufan Wang, Daniel Sheldon:
Parametric Bootstrap for Differentially Private Confidence Intervals. 1598-1618 - Alex Delalande:
Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport. 1619-1642 - Cristian I. Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot:
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. 1643-1654 - Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrovic, Ronitt Rubinfeld:
Online Page Migration with ML Advice. 1655-1670 - Guanhua Chen, Xiaomao Li, Menggang Yu:
Policy Learning for Optimal Individualized Dose Intervals. 1671-1693 - Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs. 1694-1711 - Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar:
Online Learning for Unknown Partially Observable MDPs. 1712-1732 - Lisha Chen, Tianyi Chen:
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably? 1733-1774 - Thomas S. Richardson, Yu Liu, James McQueen, Doug Hains:
A Bayesian Model for Online Activity Sample Sizes. 1775-1785 - Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi:
Parallel MCMC Without Embarrassing Failures. 1786-1804 - Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. 1805-1845 - Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike:
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. 1846-1870 - Matthew J. Holland, El Mehdi Haress:
Spectral risk-based learning using unbounded losses. 1871-1886 - Donghao Ying, Yuhao Ding, Javad Lavaei:
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization. 1887-1909 - Yuhao Ding, Junzi Zhang, Javad Lavaei:
On the Global Optimum Convergence of Momentum-based Policy Gradient. 1910-1934 - Benjamin Poignard, Peter J. Naylor, Héctor Climente-González, Makoto Yamada:
Feature screening with kernel knockoffs. 1935-1974 - Danny Wood, Tingting Mu, Gavin Brown:
Bias-Variance Decompositions for Margin Losses. 1975-2001 - Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew B. Duncan:
Grassmann Stein Variational Gradient Descent. 2002-2021 - Dirk van der Hoeven, Nicolò Cesa-Bianchi:
Nonstochastic Bandits and Experts with Arm-Dependent Delays. 2022-2044 - Ruo-Chun Tzeng, Po-An Wang, Florian Adriaens, Aristides Gionis, Chi-Jen Lu:
Improved analysis of randomized SVD for top-eigenvector approximation. 2045-2072 - Alexander Munteanu, Simon Omlor, Christian Peters:
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets. 2073-2100 - Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. 2101-2133 - Marius Memmel, Puze Liu, Davide Tateo, Jan Peters:
Dimensionality Reduction and Prioritized Exploration for Policy Search. 2134-2157 - Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys:
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? 2158-2172 - Piyushi Manupriya, Tarun Ram Menta, Saketha Nath Jagarlapudi, Vineeth N. Balasubramanian:
Improving Attribution Methods by Learning Submodular Functions. 2173-2190 - Elias Samuel Wirth, Sebastian Pokutta:
Conditional Gradients for the Approximately Vanishing Ideal. 2191-2209 - Dorian Baudry, Yoan Russac, Emilie Kaufmann:
Efficient Algorithms for Extreme Bandits. 2210-2248 - Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
Rejection sampling from shape-constrained distributions in sublinear time. 2249-2265 - Siu Lun Chau, Javier González, Dino Sejdinovic:
Learning Inconsistent Preferences with Gaussian Processes. 2266-2281 - Susanne Trick, Constantin A. Rothkopf:
Bayesian Classifier Fusion with an Explicit Model of Correlation. 2282-2310 - Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Conditionally Gaussian PAC-Bayes. 2311-2329 - Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag:
Leveraging Time Irreversibility with Order-Contrastive Pre-training. 2330-2353 - Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee:
Moment Matching Deep Contrastive Latent Variable Models. 2354-2371 - Frederik Benzing:
Unifying Importance Based Regularisation Methods for Continual Learning. 2372-2396 - Adrian Rivera Cardoso, Ryan Rogers:
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown. 2397-2419 - Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári:
Confident Least Square Value Iteration with Local Access to a Simulator. 2420-2435 - Ruihao Zhu, Branislav Kveton:
Safe Optimal Design with Applications in Off-Policy Learning. 2436-2447 - Salim I. Amoukou, Tangi Salaün, Nicolas J.-B. Brunel:
Accurate Shapley Values for explaining tree-based models. 2448-2465 - Tianyi Chen, Yuejiao Sun, Quan Xiao, Wotao Yin:
A Single-Timescale Method for Stochastic Bilevel Optimization. 2466-2488 - Lydia T. Liu, Nikhil Garg, Christian Borgs:
Strategic ranking. 2489-2518 - Evrard Garcelon, Matteo Pirotta, Vianney Perchet:
Encrypted Linear Contextual Bandit. 2519-2551 - Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. 2552-2573 - Jihun Yun, Aurélie C. Lozano, Eunho Yang:
AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. 2574-2606 - Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin E. Tripp, Yuejie Chi:
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion. 2607-2617 - Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. 2618-2640 - Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. 2641-2657 - Jiawei Huang, Nan Jiang:
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. 2658-2705 - Oliver E. Richardson:
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function. 2706-2735 - Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. 2736-2761 - Hajime Ono, Kazuhiro Minami, Hideitsu Hino:
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation. 2762-2783 - Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao:
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. 2784-2802 - Hoyoung Kim, Seunghyuk Cho, Dongwoo Kim, Jungseul Ok:
Robust Deep Learning from Crowds with Belief Propagation. 2803-2822 - Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Sampling from Arbitrary Functions via PSD Models. 2823-2861 - Rui Tuo, Wenjia Wang:
Uncertainty Quantification for Bayesian Optimization. 2862-2884 - Amit Peleg, Naama Pearl, Ron Meir:
Metalearning Linear Bandits by Prior Update. 2885-2926 - Kazu Ghalamkari, Mahito Sugiyama:
Fast Rank-1 NMF for Missing Data with KL Divergence. 2927-2940 - Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Randomized Stochastic Gradient Descent Ascent. 2941-2969 - Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell:
Aligned Multi-Task Gaussian Process. 2970-2988 - Emilien Dupont, Yee Whye Teh, Arnaud Doucet:
Generative Models as Distributions of Functions. 2989-3015 - Lukas Fromme, Jasmina Bogojeska, Jonas Kuhn:
ContextGen: Targeted Data Generation for Low Resource Domain Specific Text Classification. 3016-3027 - Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa:
Super-Acceleration with Cyclical Step-sizes. 3028-3065 - Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj:
On PAC-Bayesian reconstruction guarantees for VAEs. 3066-3079 - Alexander Bartler, Andre Bühler, Felix Wiewel, Mario Döbler, Bin Yang:
MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption. 3080-3090 - Rong Zhu, Branislav Kveton:
Random Effect Bandits. 3091-3107 - Matti Karppa, Martin Aumüller, Rasmus Pagh:
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. 3108-3137 - Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky:
Embedded Ensembles: infinite width limit and operating regimes. 3138-3163