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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 - Qiang Li, Hoi-To Wai:
State Dependent Performative Prediction with Stochastic Approximation. 3164-3186 - Mirco Mutti, Stefano Del Col, Marcello Restelli:
Reward-Free Policy Space Compression for Reinforcement Learning. 3187-3203 - Matías Altamirano, Felipe A. Tobar:
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures. 3204-3218 - Souhaib Ben Taieb:
Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. 3219-3241 - Jason Milionis, Alkis Kalavasis, Dimitris A. Fotakis, Stratis Ioannidis:
Differentially Private Regression with Unbounded Covariates. 3242-3273 - Honghao Wei, Xin Liu, Lei Ying:
Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation. 3274-3307 - William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick:
Measuring the robustness of Gaussian processes to kernel choice. 3308-3331 - Rui Yuan, Robert M. Gower, Alessandro Lazaric:
A general sample complexity analysis of vanilla policy gradient. 3332-3380 - Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. 3381-3417 - Torty Sivill, Peter A. Flach:
LIMESegment: Meaningful, Realistic Time Series Explanations. 3418-3433 - Ben Adlam, Jake A. Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. 3434-3457 - Takashi Furuya, Kazuma Suetake, Koichi Taniguchi, Hiroyuki Kusumoto, Ryuji Saiin, Tomohiro Daimon:
Spectral Pruning for Recurrent Neural Networks. 3458-3482 - Tin D. Nguyen, Brian L. Trippe, Tamara Broderick:
Many processors, little time: MCMC for partitions via optimal transport couplings. 3483-3514 - Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. 3515-3530 - Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos:
Finding Nearly Everything within Random Binary Networks. 3531-3541 - Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. 3542-3555 - Arman Adibi, Aryan Mokhtari, Hamed Hassani:
Minimax Optimization: The Case of Convex-Submodular. 3556-3580 - John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba:
Federated Learning with Buffered Asynchronous Aggregation. 3581-3607 - Jiajing Zheng, Alexander D'Amour, Alexander Franks:
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. 3608-3626 - Chengkuan Hong, Christian R. Shelton:
Deep Neyman-Scott Processes. 3627-3646 - Jones Yirui Liu, Xinghao Qiao, Jessica Lam:
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models. 3647-3662 - Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth:
Certifiably Robust Variational Autoencoders. 3663-3683 - Kaiwen Zhou, Lai Tian, Anthony Man-Cho So, James Cheng:
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums. 3684-3708 - Felix Biggs, Benjamin Guedj:
On Margins and Derandomisation in PAC-Bayes. 3709-3731 - Oshrat Bar, Amnon Drory, Raja Giryes:
A Spectral Perspective of DNN Robustness to Label Noise. 3732-3752 - Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. 3753-3771 - Lai Tian, Anthony Man-Cho So:
Computing D-Stationary Points of ρ-Margin Loss SVM. 3772-3793 - Xuhui Zhang, José H. Blanchet, Soumyadip Ghosh, Mark S. Squillante:
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality. 3794-3820 - Berivan Isik, Tsachy Weissman, Albert No:
An Information-Theoretic Justification for Model Pruning. 3821-3846 - Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran:
Flexible Accuracy for Differential Privacy. 3847-3882 - Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. 3883-3913 - Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi, Shi Li, Di Wang:
On Facility Location Problem in the Local Differential Privacy Model. 3914-3929 - Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. 3930-3961 - Tony Ginart, Martin Jinye Zhang, James Zou:
MLDemon: Deployment Monitoring for Machine Learning Systems. 3962-3997 - Zachary Izzo, James Zou, Lexing Ying:
How to Learn when Data Gradually Reacts to Your Model. 3998-4035 - Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer:
Mitigating Bias in Calibration Error Estimation. 4036-4054 - Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri:
Privacy Amplification by Subsampling in Time Domain. 4055-4069 - Shivam Garg, Santosh S. Vempala:
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization. 4070-4108 - Jingfeng Wu, Vladimir Braverman, Lin Yang:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. 4109-4131 - Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. 4132-4157 - Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar:
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. 4158-4177 - Lei Wu:
Learning a Single Neuron for Non-monotonic Activation Functions. 4178-4197 - Jiaye Teng, Weiran Huang, Haowei He:
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis. 4198-4216 - Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood:
Model-free Policy Learning with Reward Gradients. 4217-4234 - Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy:
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. 4235-4258 - Jiafan He, Dongruo Zhou, Quanquan Gu:
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. 4259-4280 - Kiran Koshy Thekumparampil, Niao He, Sewoong Oh:
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. 4281-4308 - Fan Wang, Oscar Hernan Madrid Padilla, Yi Yu, Alessandro Rinaldo:
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. 4309-4338 - Yi Yu, Oscar Hernan Madrid Padilla, Alessandro Rinaldo:
Optimal partition recovery in general graphs. 4339-4358 - Jungtaek Kim, Seungjin Choi:
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. 4359-4375 - Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. 4376-4396 - Khang Le, Huy Nguyen, Khai Nguyen, Tung Pham, Nhat Ho:
On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity. 4397-4413 - Roy Fox, Stephen M. McAleer, Will Overman, Ioannis Panageas:
Independent Natural Policy Gradient always converges in Markov Potential Games. 4414-4425 - Beomsu Kim, Junghoon Seo:
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness. 4426-4445 - Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari:
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. 4446-4461 - Larissa T. Triess, Andre Bühler, David Peter, Fabian B. Flohr, Marius Zöllner:
Point Cloud Generation with Continuous Conditioning. 4462-4481 - Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik:
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. 4482-4498 - Ana Lucic, Maartje A. ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri:
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. 4499-4511 - Cen-You Li, Barbara Rakitsch, Christoph Zimmer:
Safe Active Learning for Multi-Output Gaussian Processes. 4512-4551 - Minyoung Kim, Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic:
Variational Continual Proxy-Anchor for Deep Metric Learning. 4552-4573 - Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju:
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. 4574-4594 - Alexander Camuto, Matthew Willetts:
Variational Autoencoders: A Harmonic Perspective. 4595-4611 - Alex Nowak, Alessandro Rudi, Francis R. Bach:
On the Consistency of Max-Margin Losses. 4612-4633 - Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf:
A prior-based approximate latent Riemannian metric. 4634-4658 - Junsoo Ha, Gunhee Kim:
On Convergence of Lookahead in Smooth Games. 4659-4684 - Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Learning Proposals for Practical Energy-Based Regression. 4685-4704 - Edward De Brouwer, Javier Gonzalez, Stephanie L. Hyland:
Predicting the impact of treatments over time with uncertainty aware neural differential equations. 4705-4722 - Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R. Srikant:
Improved Algorithms for Misspecified Linear Markov Decision Processes. 4723-4746 - Max Baak, Simon Brugman, Ilan Fridman Rojas, Lorraine Dalmeida, Ralph E. Q. Urlus, Jean-Baptiste Oger:
Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data. 4747-4763 - Marcelo Hartmann, Mark Girolami, Arto Klami:
Lagrangian manifold Monte Carlo on Monge patches. 4764-4781 - Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang:
Optimal Accounting of Differential Privacy via Characteristic Function. 4782-4817 - Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong:
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. 4818-4834 - Felix L. Opolka, Pietro Liò:
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes. 4835-4852 - Jorge Silva, Felipe A. Tobar:
On the Interplay between Information Loss and Operation Loss in Representations for Classification. 4853-4871 - Georgios Arvanitidis, Miguel González Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg:
Pulling back information geometry. 4872-4894 - Preetish Rath, Michael C. Hughes:
Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint. 4895-4914 - Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. 4915-4936 - Chuyang Ke, Jean Honorio:
Federated Myopic Community Detection with One-shot Communication. 4937-4954 - George Wynne, Veit Wild:
Variational Gaussian Processes: A Functional Analysis View. 4955-4971 - Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf:
Adversarially Robust Kernel Smoothing. 4972-4994 - Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré:
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe. 4995-5021 - Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment for Markov Decision Processes. 5022-5050 - Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. 5051-5083 - Ni Ding:
Kantorovich Mechanism for Pufferfish Privacy. 5084-5103 - Tom Yan, Chicheng Zhang:
Margin-distancing for safe model explanation. 5104-5134 - Dávid Terjék, Diego González-Sánchez:
Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm. 5135-5165 - Wessel P. Bruinsma, Martin Tegner, Richard E. Turner:
Modelling Non-Smooth Signals with Complex Spectral Structure. 5166-5195 - Alain Rakotomamonjy, Rémi Flamary, Joseph Salmon, Gilles Gasso:
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. 5196-5211 - Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung:
Particle-based Adversarial Local Distribution Regularization. 5212-5224 - Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger:
Crowdsourcing Regression: A Spectral Approach. 5225-5242 - Damien Garreau:
How to scale hyperparameters for quickshift image segmentation. 5243-5275 - Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez:
Wide Mean-Field Bayesian Neural Networks Ignore the Data. 5276-5333 - Edwige Cyffers, Aurélien Bellet:
Privacy Amplification by Decentralization. 5334-5353 - Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Animashree Anandkumar:
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems. 5354-5390 - Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman:
New Coresets for Projective Clustering and Applications. 5391-5415 - Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai:
Weak Separation in Mixture Models and Implications for Principal Stratification. 5416-5458 - Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson:
Using time-series privileged information for provably efficient learning of prediction models. 5459-5484 - Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. 5485-5517 - Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, Greg Mori:
TD-GEN: Graph Generation Using Tree Decomposition. 5518-5537 - Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja:
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. 5538-5562 - Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. 5563-5582 - Vibhor Porwal, Piyush Srivastava, Gaurav Sinha:
Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions. 5583-5603 - Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka:
Estimators of Entropy and Information via Inference in Probabilistic Models. 5604-5621 - Yuqing Zhu, Yu-Xiang Wang:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. 5622-5635 - Pierre Ablin, Gabriel Peyré:
Fast and accurate optimization on the orthogonal manifold without retraction. 5636-5657 - Romain Laroche, Remi Tachet des Combes:
Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms. 5658-5688 - Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard:
Efficient Kernelized UCB for Contextual Bandits. 5689-5720 - Ye Tian, Gesualdo Scutari, Tianyu Cao, Alexander V. Gasnikov:
Acceleration in Distributed Optimization under Similarity. 5721-5756 - Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Corruption-robust Offline Reinforcement Learning. 5757-5773 - Alix Lheritier, Nicolas Bondoux:
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning. 5774-5789 - Kirill Neklyudov, Max Welling:
Orbital MCMC. 5790-5814 - Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet J. Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J. Ribeiro, Shivaram Kalyanakrishnan:
PAC Mode Estimation using PPR Martingale Confidence Sequences. 5815-5852 - Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya K. Muthukumar:
Harmless interpolation in regression and classification with structured features. 5853-5875 - Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Masked Training of Neural Networks with Partial Gradients. 5876-5890 - Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Learning in Stochastic Monotone Games with Decision-Dependent Data. 5891-5912 - Rishi Saket, Aravindan Raghuveer, Balaraman Ravindran:
On Combining Bags to Better Learn from Label Proportions. 5913-5927 - Robert Müller, Aldo Pacchiano:
Meta Learning MDPs with linear transition models. 5928-5948 - James A. Brofos, Marylou Gabrié, Marcus A. Brubaker, Roy R. Lederman:
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. 5949-5986 - Horace Pan, Risi Kondor:
Permutation Equivariant Layers for Higher Order Interactions. 5987-6001 - Gerardo Duran-Martin, Aleyna Kara, Kevin Murphy:
Efficient Online Bayesian Inference for Neural Bandits. 6002-6021 - Jongha J. Ryu, Alankrita Bhatt, Young-Han Kim:
Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting. 6022-6044 - Gavin Brown, Shlomi Hod, Iden Kalemaj:
Performative Prediction in a Stateful World. 6045-6061 - Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson:
Learning Revenue-Maximizing Auctions With Differentiable Matching. 6062-6073 - Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Kapil Jayesh Pathak:
Orthogonal Multi-Manifold Enriching of Directed Networks. 6074-6086 - Pratik Patil, Alessandro Rinaldo, Ryan J. Tibshirani:
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. 6087-6120 - Yujie Wang, Mike Izbicki:
The Tree Loss: Improving Generalization with Many Classes. 6121-6133 - Michalis K. Titsias, Jiaxin Shi:
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. 6134-6151 - Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska:
Transfer Learning with Gaussian Processes for Bayesian Optimization. 6152-6181 - Leda Liang, Brendan Juba:
Conditional Linear Regression for Heterogeneous Covariances. 6182-6199 - Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. 6200-6224 - Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis:
Node Feature Kernels Increase Graph Convolutional Network Robustness. 6225-6241 - Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta:
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. 6242-6269 - Staal Amund Vinterbo:
Differential privacy for symmetric log-concave mechanisms. 6270-6291 - Yujia Wang, Lu Lin, Jinghui Chen:
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization. 6292-6320 - Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti:
Fair Disaster Containment via Graph-Cut Problems. 6321-6333 - Xinyuan Cao, Weiyang Liu, Santosh S. Vempala:
Provable Lifelong Learning of Representations. 6334-6356 - Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady:
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. 6357-6386 - Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas:
A New Notion of Individually Fair Clustering: α-Equitable k-Center. 6387-6408 - Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye:
Iterative Alignment Flows. 6409-6444 - Vincent Hsiao, Dana S. Nau, Rina Dechter:
Fast Fourier Transform Reductions for Bayesian Network Inference. 6445-6458 - Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. 6459-6500 - Moritz P. Hoffmann, Tanya Braun, Ralf Möller:
Lifted Division for Lifted Hugin Belief Propagation. 6501-6510 - Charlotte Bunne, Laetitia Papaxanthos, Andreas Krause, Marco Cuturi:
Proximal Optimal Transport Modeling of Population Dynamics. 6511-6528 - Chuanhao Li, Hongning Wang:
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. 6529-6553 - Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. 6554-6572 - Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan, Hossein Azizpour, Mårten Björkman:
Are All Linear Regions Created Equal? 6573-6590 - Joshua Agterberg, Jeremias Sulam:
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. 6591-6629 - Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood:
An Alternate Policy Gradient Estimator for Softmax Policies. 6630-6689 - Yuan Wu, Diana Inkpen, Ahmed El-Roby:
Co-Regularized Adversarial Learning for Multi-Domain Text Classification. 6690-6701 - Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff:
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. 6702-6734 - Yinglun Zhu, Julian Katz-Samuels, Robert D. Nowak:
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. 6735-6769 - Benito van der Zander, Marcel Wienöbst, Markus Bläser, Maciej Liskiewicz:
Identification in Tree-shaped Linear Structural Causal Models. 6770-6792 - Yinglun Zhu, Robert D. Nowak:
Pareto Optimal Model Selection in Linear Bandits. 6793-6813 - Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil:
PAC Top-k Identification under SST in Limited Rounds. 6814-6839 - Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi:
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time. 6840-6861 - Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake:
Dual-Level Adaptive Information Filtering for Interactive Image Segmentation. 6862-6879 - Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin:
On the Value of Prior in Online Learning to Rank. 6880-6892 - Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim:
Doubly Mixed-Effects Gaussian Process Regression. 6893-6908 - Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff:
Weighted Gaussian Process Bandits for Non-stationary Environments. 6909-6932 - Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi:
Conditionally Tractable Density Estimation using Neural Networks. 6933-6946 - Hsu Kao, Vijay G. Subramanian:
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning. 6947-6967 - Mojmir Mutny, Andreas Krause:
Sensing Cox Processes via Posterior Sampling and Positive Bases. 6968-6989 - Edwin Fong, Brieuc Lehmann:
A Predictive Approach to Bayesian Nonparametric Survival Analysis. 6990-7013 - Laura Balzano:
On the equivalence of Oja's algorithm and GROUSE. 7014-7030 - Elvis Nava, Mojmir Mutny, Andreas Krause:
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes. 7031-7054 - Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii:
Label differential privacy via clustering. 7055-7075 - Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris C. Holmes:
Neural score matching for high-dimensional causal inference. 7076-7110 - Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. 7111-7123 - Boris Ndjia Njike, Xavier Siebert:
Multi-class classification in nonparametric active learning. 7124-7162 - Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei:
On the Assumptions of Synthetic Control Methods. 7163-7175 - Anant Raj, Pooria Joulani, András György, Csaba Szepesvári:
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. 7176-7186 - Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra:
Investigating the Role of Negatives in Contrastive Representation Learning. 7187-7209 - AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen:
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference. 7210-7239 - Dominic Danks, Christopher Yau:
Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis. 7240-7256 - Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld, Bharath K. Sriperumbudur:
Cycle Consistent Probability Divergences Across Different Spaces. 7257-7285 - Yingyi Ma, Xinhua Zhang:
Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning. 7286-7299 - Sebastian Bordt, Ulrike von Luxburg:
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. 7300-7319 - Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco:
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization. 7320-7348 - Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Adaptive Multi-Goal Exploration. 7349-7383 - Subhojyoti Mukherjee, Ardhendu S. Tripathy, Robert D. Nowak:
Chernoff Sampling for Active Testing and Extension to Active Regression. 7384-7432 - Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili, Laura Toni, Miguel R. D. Rodrigues:
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift. 7433-7449 - Eli N. Weinstein, Alan Nawzad Amin, Will S. Grathwohl, Daniel Kassler, Jean Disset, Debora S. Marks:
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models. 7450-7482 - Ruida Zhou, Chao Tian:
Approximate Top-m Arm Identification with Heterogeneous Reward Variances. 7483-7504 - Arash A. Amini, Bryon Aragam, Qing Zhou:
On perfectness in Gaussian graphical models. 7505-7517 - Sumedh A. Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. 7518-7530 - Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, N. Variyam Vinodchandran:
Efficient interventional distribution learning in the PAC framework. 7531-7549 - Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
Dropout as a Regularizer of Interaction Effects. 7550-7564 - Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier:
Thompson Sampling with a Mixture Prior. 7565-7586 - Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal:
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. 7587-7624 - Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin G. Jamieson, Robert D. Nowak:
Nearly Optimal Algorithms for Level Set Estimation. 7625-7658 - Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse:
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. 7659-7679 - Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. 7680-7723 - Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh:
Hierarchical Bayesian Bandits. 7724-7741 - Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud:
Complex Momentum for Optimization in Games. 7742-7765 - Hao Wu, Anthony Wirth:
Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches. 7766-7798 - Matthew D. Hoffman, Pavel Sountsov:
Tuning-Free Generalized Hamiltonian Monte Carlo. 7799-7813 - Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi:
Federated Functional Gradient Boosting. 7814-7840 - Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised GPLVM with Stochastic Variational Inference. 7841-7864 - Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou:
Stochastic Extragradient: General Analysis and Improved Rates. 7865-7901 - Axel Brando, Joan Gimeno, José A. Rodríguez-Serrano, Jordi Vitrià:
Deep Non-crossing Quantiles through the Partial Derivative. 7902-7914 - Yeonwoo Jeong, Deokjae Lee, Gaon An, Changyong Son, Hyun Oh Song:
Optimal channel selection with discrete QCQP. 7915-7941 - Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. 7942-7975 - Hugh Dance, Brooks Paige:
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. 7976-8002 - Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. 8003-8021 - Jie Wang, Rui Gao, Yao Xie:
Two-Sample Test with Kernel Projected Wasserstein Distance. 8022-8055 - Hiroaki Sasaki, Jun-Ichiro Hirayama, Takafumi Kanamori:
Mode estimation on matrix manifolds: Convergence and robustness. 8056-8079 - Alex R. Dytso, Mario Goldenbaum, H. Vincent Poor, Shlomo Shamai:
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence. 8080-8094 - Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. 8095-8111 - Samory Kpotufe, Gan Yuan, Yunfan Zhao:
Nuances in Margin Conditions Determine Gains in Active Learning. 8112-8126 - Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang:
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. 8127-8150 - Murat A. Erdogdu, Rasa Hosseinzadeh, Shunshi Zhang:
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. 8151-8175 - Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang:
On the Convergence of Continuous Constrained Optimization for Structure Learning. 8176-8198 - Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Hardness of Learning a Single Neuron with Adversarial Label Noise. 8199-8213 - Zhaobin Kuang, Chidubem G. Arachie, Bangyong Liang, Pradyumna Narayana, Giulia DeSalvo, Michael S. Quinn, Bert Huang, Geoffrey Downs, Yang Yang:
Firebolt: Weak Supervision Under Weaker Assumptions. 8214-8259 - Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi:
Relational Neural Markov Random Fields. 8260-8269 - Warren R. Morningstar, Alex Alemi, Joshua V. Dillon:
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. 8270-8298 - Devansh Bisla, Jing Wang, Anna Choromanska:
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape. 8299-8339 - Aaron J. Fisher:
Online Control of the False Discovery Rate under "Decision Deadlines". 8340-8359 - Shuai Xiao, Zaifan Jiang, Shuang Yang:
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation. 8360-8369 - Zai Shi, Atilla Eryilmaz:
A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints. 8370-8391 - Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood:
Amortized Rejection Sampling in Universal Probabilistic Programming. 8392-8412 - Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
The Curse of Passive Data Collection in Batch Reinforcement Learning. 8413-8438 - Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever:
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. 8439-8457 - Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis:
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory. 8458-8492 - Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman:
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. 8493-8513 - Ningyuan Chen, Xuefeng Gao, Yi Xiong:
Debiasing Samples from Online Learning Using Bootstrap. 8514-8533 - Geelon So, Gaurav Mahajan, Sanjoy Dasgupta:
Convergence of online k-means. 8534-8569 - Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier:
Reconstructing Test Labels from Noisy Loss Functions. 8570-8591 - Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski:
Contrasting the landscape of contrastive and non-contrastive learning. 8592-8618 - Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. 8619-8649 - Joshua K. Behne, Galen Reeves:
Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity. 8650-8672 - Yuheng Bu, Gholamali Aminian, Laura Toni, Gregory W. Wornell, Miguel R. D. Rodrigues:
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm. 8673-8699 - Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. 8700-8714 - Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization. 8715-8737 - Ming Gao, Wai Ming Tai, Bryon Aragam:
Optimal estimation of Gaussian DAG models. 8738-8757 - Ali Vakilian, Mustafa Yalçiner:
Improved Approximation Algorithms for Individually Fair Clustering. 8758-8779 - Yongchan Kwon, James Zou:
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. 8780-8802 - Guillaume G. Martinet, Alexander Strzalkowski, Barbara E. Engelhardt:
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction. 8803-8851 - Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu:
Online Continual Adaptation with Active Self-Training. 8852-8883 - Honggang Wang, Anirban Bhattacharya, Debdeep Pati, Yun Yang:
Structured variational inference in Bayesian state-space models. 8884-8905 - Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang:
Structured Multi-task Learning for Molecular Property Prediction. 8906-8920 - Masahiro Nakano, Ryo Nishikimi, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Nonparametric Relational Models with Superrectangulation. 8921-8937 - Sarah Huiyi Cen, Devavrat Shah:
Regret, stability & fairness in matching markets with bandit learners. 8938-8968 - Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju:
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. 8969-8996 - Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. 8997-9016 - Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein:
ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics. 9017-9049 - Margalit R. Glasgow, Honglin Yuan, Tengyu Ma:
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. 9050-9090 - Yizhou Chen, Shizhuo Zhang, Bryan Kian Hsiang Low:
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. 9091-9113 - Zhenlin Wang, Andrew J. Wagenmaker, Kevin G. Jamieson:
Best Arm Identification with Safety Constraints. 9114-9146 - Jiaojiao Fan, Isabel Haasler, Johan Karlsson, Yongxin Chen:
On the complexity of the optimal transport problem with graph-structured cost. 9147-9165 - Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone:
Second-Order Sensitivity Analysis for Bilevel Optimization. 9166-9181 - Soumyabrata Pal, Arya Mazumdar:
On Learning Mixture Models with Sparse Parameters. 9182-9213 - Yubo Zhuang, Xiaohui Chen, Yun Yang:
Sketch-and-lift: scalable subsampled semidefinite program for K-means clustering. 9214-9246 - Margalit R. Glasgow, Mary Wootters:
Asynchronous Distributed Optimization with Stochastic Delays. 9247-9279 - Yizhe Xu, Steve Yadlowsky:
Calibration Error for Heterogeneous Treatment Effects. 9280-9303 - Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. 9304-9333 - Robin A. Brown, Edward Schmerling, Navid Azizan, Marco Pavone:
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming. 9334-9355 - Ashish Katiyar, Soumya Basu, Vatsal Shah, Constantine Caramanis:
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise. 9356-9399 - Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang:
Learning Sparse Fixed-Structure Gaussian Bayesian Networks. 9400-9429 - Kulin Shah, Amit Deshpande, Navin Goyal:
Learning and Generalization in Overparameterized Normalizing Flows. 9430-9504 - Clayton Hendrick Sanford, Vaggos Chatziafratis:
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem. 9505-9549 - Arman Serikuly Zharmagambetov, Miguel Á. Carreira-Perpiñán:
Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings. 9550-9570 - Matthew Engelhard, Ricardo Henao:
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data. 9571-9581 - Kishan Panaganti, Dileep M. Kalathil:
Sample Complexity of Robust Reinforcement Learning with a Generative Model. 9582-9602 - Rachit Chhaya, Anirban Dasgupta, Jayesh Choudhari, Supratim Shit:
On Coresets for Fair Regression and Individually Fair Clustering. 9603-9625 - Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. 9626-9642 - Jianyu Xu, Yu-Xiang Wang:
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. 9643-9662 - Yan Shuo Tan, Abhineet Agarwal, Bin Yu:
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds. 9663-9685 - Xiaolu Wang, Peng Wang, Anthony Man-Cho So:
Exact Community Recovery over Signed Graphs. 9686-9710 - Eduardo Pavez:
Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency. 9711-9722 - Daniil Tiapkin, Alexander V. Gasnikov:
Primal-Dual Stochastic Mirror Descent for MDPs. 9723-9740 - Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Convex Analysis of the Mean Field Langevin Dynamics. 9741-9757 - Sharu Theresa Jose, Sangwoo Park, Osvaldo Simeone:
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning. 9758-9775 - Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts:
Marginalising over Stationary Kernels with Bayesian Quadrature. 9776-9792 - Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan:
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. 9793-9826 - Stephen Sheng, Keerthi Vasan G. C, Chi Po P. Choi, James Sharpnack, Tucker Jones:
An Unsupervised Hunt for Gravitational Lenses. 9827-9843 - Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen:
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics. 9844-9868 - Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Mean Nyström Embeddings for Adaptive Compressive Learning. 9869-9889 - Da Sun Handason Tam, Siyue Xie, Wing Cheong Lau:
GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures. 9890-9907 - Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. 9908-9942 - Sarbojit Roy, Jyotishka Ray Choudhury, Subhajit Dutta:
On Some Fast And Robust Classifiers For High Dimension, Low Sample Size Data. 9943-9968 - Sofia Ek, Dave Zachariah, Peter Stoica:
Learning Pareto-Efficient Decisions with Confidence. 9969-9981 - Harish Doddi, Deepjyoti Deka, Saurav Talukdar, Murti V. Salapaka:
Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs. 9982-9997 - Malte Nalenz, Thomas Augustin:
Compressed Rule Ensemble Learning. 9998-10014 - Nathanael Bosch, Filip Tronarp, Philipp Hennig:
Pick-and-Mix Information Operators for Probabilistic ODE Solvers. 10015-10027 - Khaled Eldowa, Lorenzo Bisi, Marcello Restelli:
Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse Reinforcement Learning. 10028-10066 - Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini:
The Importance of Future Information in Credit Card Fraud Detection. 10067-10077 - Antoine Barrier, Aurélien Garivier, Tomás Kocák:
A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits. 10078-10109 - Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut:
Differentially Private Federated Learning on Heterogeneous Data. 10110-10145 - Augustin Chevallier, Frédéric Cazals, Paul Fearnhead:
Efficient computation of the the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes. 10146-10160 - Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaïd Harchaoui:
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates. 10161-10195 - YooJung Choi, Tal Friedman, Guy Van den Broeck:
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations. 10196-10208 - Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. 10209-10233 - Yassir Jedra, Alexandre Proutière:
Minimal Expected Regret in Linear Quadratic Control. 10234-10321 - Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein:
Data-splitting improves statistical performance in overparameterized regimes. 10322-10350 - Yae Jee Cho, Jianyu Wang, Gauri Joshi:
Towards Understanding Biased Client Selection in Federated Learning. 10351-10375 - Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi:
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. 10376-10406 - Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani:
Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference. 10407-10421 - Prem Talwai, Ali Shameli, David Simchi-Levi:
Sobolev Norm Learning Rates for Conditional Mean Embeddings. 10422-10447 - Reinhard Heckel:
Provable Continual Learning via Sketched Jacobian Approximations. 10448-10470 - Fei Gao, Jiang Zhang, Yan Zhang:
Neural Enhanced Dynamic Message Passing. 10471-10482 - Paulina Tomaszewska, Adam Zychowski, Jacek Mandziuk:
Duel-based Deep Learning system for solving IQ tests. 10483-10492 - Rui Wang, Wangli Xu:
On a Connection Between Fast and Sparse Oblivious Subspace Embeddings. 10493-10517 - Yannick Rudolph, Ulf Brefeld:
Modeling Conditional Dependencies in Multiagent Trajectories. 10518-10533 - Lenon Minorics, Ali Caner Türkmen, David Kernert, Patrick Blöbaum, Laurent Callot, Dominik Janzing:
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies. 10534-10554 - Ilan Price, Stephan Rasp:
Increasing the accuracy and resolution of precipitation forecasts using deep generative models. 10555-10571 - Guillaume Wang, Konstantin Donhauser, Fanny Yang:
Tight bounds for minimum ℓ1-norm interpolation of noisy data. 10572-10602 - Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. 10603-10621 - Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. 10622-10639 - Alexander V. Nikitin, S. T. John, Arno Solin, Samuel Kaski:
Non-separable Spatio-temporal Graph Kernels via SPDEs. 10640-10660 - Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. 10661-10686 - Oskar Kviman, Harald Melin, Hazal Koptagel, Victor Elvira, Jens Lagergren:
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations. 10687-10702 - Arne Nix, Suhas Shrinivasan, Edgar Y. Walker, Fabian H. Sinz:
Can Functional Transfer Methods Capture Simple Inductive Biases? 10703-10717 - Nicolas Emmenegger, Rasmus Kyng, Ahad N. Zehmakan:
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization. 10718-10752 - Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen:
Model-agnostic out-of-distribution detection using combined statistical tests. 10753-10776 - Xiwei Cheng, Sidharth Jaggi, Qiaoqiao Zhou:
Generalized Group Testing. 10777-10835 - Yang Liu, Yifan Zhou, Ping Li, Feifang Hu:
Adaptive A/B Test on Networks with Cluster Structures. 10836-10851 - Flavio Chierichetti, Alessandro Panconesi, Giuseppe Re, Luca Trevisan:
Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models. 10852-10880 - Arshdeep Sekhon, Zhe Wang, Yanjun Qi:
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge. 10881-10923 - Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo:
Two-way Sparse Network Inference for Count Data. 10924-10938 - Quan Zhou, Aaron Smith:
Rapid Convergence of Informed Importance Tempering. 10939-10965 - Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson:
On the Implicit Bias of Gradient Descent for Temporal Extrapolation. 10966-10981 - Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis:
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion. 10982-10998 - Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeniy Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vyalov, Anatolii Zaikovskii, Aleksandr Petiushko:
Quadric Hypersurface Intersection for Manifold Learning in Feature Space. 10999-11013 - Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. 11014-11036 - David A. Bruns-Smith, Avi Feller:
Outcome Assumptions and Duality Theory for Balancing Weights. 11037-11055 - Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl:
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. 11056-11071 - Peilin Yu, Tiffany Ding, Stephen H. Bach:
Learning from Multiple Noisy Partial Labelers. 11072-11095 - Michail Fasoulakis, Evangelos Markakis, Yannis Pantazis, Constantinos Varsos:
Forward Looking Best-Response Multiplicative Weights Update Methods for Bilinear Zero-sum Games. 11096-11117 - Bahman Pedrood, Carlotta Domeniconi, Kathryn B. Laskey:
Hypergraph Simultaneous Generators. 11118-11130 - Joel Dyer, Patrick W. Cannon, Sebastian M. Schmon:
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation. 11131-11144 - Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. 11145-11168 - Nathan Kallus, Angela Zhou:
Stateful Offline Contextual Policy Evaluation and Learning. 11169-11194 - Zaiwei Chen, Siva Theja Maguluri:
Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation. 11195-11214 - Osama A. Hanna, Lin Yang, Christina Fragouli:
Solving Multi-Arm Bandit Using a Few Bits of Communication. 11215-11236 - Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Causal Effect Identification with Context-specific Independence Relations of Control Variables. 11237-11246 - Lang Liu, Soumik Pal, Zaïd Harchaoui:
Entropy Regularized Optimal Transport Independence Criterion. 11247-11279 - Zihao Deng, Siddartha Devic, Brendan Juba:
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. 11280-11304 - Arun Padakandla, Abram Magner:
PAC Learning of Quantum Measurement Classes : Sample Complexity Bounds and Universal Consistency. 11305-11319 - Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher G. Brinton:
Can we Generalize and Distribute Private Representation Learning? 11320-11340 - Isaac Sebenius, Topi Paananen, Aki Vehtari:
Feature Collapsing for Gaussian Process Variable Ranking. 11341-11355 - Wanrong Zhang, Yajun Mei, Rachel Cummings:
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. 11356-11373 - Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik:
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. 11374-11421 - Xinlei Xu, Awni Y. Hannun, Laurens van der Maaten:
Data Appraisal Without Data Sharing. 11422-11437 - Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung:
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. 11438-11460 - Omar Montasser, Steve Hanneke, Nathan Srebro:
Transductive Robust Learning Guarantees. 11461-11471 - Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui, Wei Chen:
Online Competitive Influence Maximization. 11472-11502 - Anna Korba, François Portier:
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff. 11503-11527 - Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
The role of optimization geometry in single neuron learning. 11528-11549 - Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal:
Learning Tensor Representations for Meta-Learning. 11550-11580 - Alireza Farhadi, MohammadTaghi Hajiaghayi, Elaine Shi:
Differentially Private Densest Subgraph. 11581-11597 - Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Mayank Goswami, Chao Chen, Dimitris N. Metaxas:
A Manifold View of Adversarial Risk. 11598-11614 - Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem. 11615-11640 - Amy E. Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti:
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks. 11641-11654 - Bahar Azari, Deniz Erdogmus:
Equivariant Deep Dynamical Model for Motion Prediction. 11655-11668 - John Paul Ryan, Sebastian E. Ament, Carla P. Gomes, Anil Damle:
The Fast Kernel Transform. 11669-11690 - Sloan Nietert, Ziv Goldfeld, Rachel Cummings:
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. 11691-11719 - Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa:
Diversity and Generalization in Neural Network Ensembles. 11720-11743
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