AISTATS 2022
492 papers
A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas Are All Linear Regions Created Equal? Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan, Hossein Azizpour, Marten Bjorkman Can We Generalize and Distribute Private Representation Learning? Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic, Maartje A. Ter Hoeve, Gabriele Tolomei, Maarten De Rijke, Fabrizio Silvestri Crowdsourcing Regression: A Spectral Approach Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger Embedded Ensembles: Infinite Width Limit and Operating Regimes Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky ExactBoost: Directly Boosting the Margin in Combinatorial and Non-Decomposable Metrics Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein Firebolt: Weak Supervision Under Weaker Assumptions Zhaobin Kuang, Chidubem G. Arachie, Bangyong Liang, Pradyumna Narayana, Giulia Desalvo, Michael S. Quinn, Bert Huang, Geoffrey Downs, Yang Yang Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption Alexander Bartler, Andre Bühler, Felix Wiewel, Mario Döbler, Bin Yang QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines Sinkformers: Transformers with Doubly Stochastic Attention Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal Stochastic Extragradient: General Analysis and Improved Rates Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou Synthsonic: Fast, Probabilistic Modeling and Synthesis of Tabular Data Max Baak, Simon Brugman, Ilan Fridman Rojas, Lorraine Dalmeida, Ralph E.Q. Urlus, Jean-Baptiste Oger TD-GEN: Graph Generation Using Tree Decomposition Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, Greg Mori A Bayesian Model for Online Activity Sample Sizes
Thomas S. Richardson, Yu Liu, James Mcqueen, Doug Hains A General Class of Surrogate Functions for Stable and Efficient Reinforcement Learning
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach A Manifold View of Adversarial Risk
Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Mayank Goswami, Chao Chen, Dimitris N. Metaxas A Prior-Based Approximate Latent Riemannian Metric
Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf A Witness Two-Sample Test
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet Acceleration in Distributed Optimization Under Similarity
Ye Tian, Gesualdo Scutari, Tianyu Cao, Alexander Gasnikov Adaptive Multi-Goal Exploration
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Michal Valko, Alessandro Lazaric Adversarially Robust Kernel Smoothing
Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf Aligned Multi-Task Gaussian Process
Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill Campbell Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schroeder De Witt, Robert Zinkov, Philip Torr, Tom Rainforth, Yee Whye Teh, Frank Wood An Alternate Policy Gradient Estimator for SoftMax Policies
Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood An Information-Theoretical Approach to Semi-Supervised Learning Under Covariate-Shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili, Laura Toni, Miguel Rodrigues An Unsupervised Hunt for Gravitational Lenses
Stephen Sheng, Keerthi Vasan G C, Chi Po P Choi, James Sharpnack, Tucker Jones Best Arm Identification with Safety Constraints
Zhenlin Wang, Andrew J. Wagenmaker, Kevin Jamieson Causally Motivated Shortcut Removal Using Auxiliary Labels
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D’Amour Certifiably Robust Variational Autoencoders
Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth Complex Momentum for Optimization in Games
Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud Conditionally Gaussian PAC-Bayes
Eugenio Clerico, George Deligiannidis, Arnaud Doucet Conditionally Tractable Density Estimation Using Neural Networks
Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi Confident Least Square Value Iteration with Local Access to a Simulator
Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari Controlling Epidemic Spread Using Probabilistic Diffusion Models on Networks
Amy E. Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti Convergence of Online K-Means
Geelon So, Gaurav Mahajan, Sanjoy Dasgupta Coresets for Data Discretization and Sine Wave Fitting
Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman Corruption-Robust Offline Reinforcement Learning
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun Cycle Consistent Probability Divergences Across Different Spaces
Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld, Bharath Sriperumbudur Data Appraisal Without Data Sharing
Xinlei Xu, Awni Hannun, Laurens Van Der Maaten Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan O. Arik, Anna Goldenberg, Tomas Pfister Deep Layer-Wise Networks Have Closed-Form Weights
Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy Deep Neyman-Scott Processes
Chengkuan Hong, Christian Shelton Deep Non-Crossing Quantiles Through the Partial Derivative
Axel Brando, Barcelona Supercomputing Center, Joan Gimeno, Jose Rodriguez-Serrano, Jordi Vitria Differentially Private Densest Subgraph
Alireza Farhadi, MohammadTaghi Hajiaghayi, Elaine Shi Differentially Private Regression with Unbounded Covariates
Jason Milionis, Alkis Kalavasis, Dimitris Fotakis, Stratis Ioannidis Dropout as a Regularizer of Interaction Effects
Benjamin J. Lengerich, Eric Xing, Rich Caruana Duel-Based Deep Learning System for Solving IQ Tests
Paulina Tomaszewska, Adam Żychowski, Jacek Mańdziuk Efficient Algorithms for Extreme Bandits
Dorian Baudry, Yoan Russac, Emilie Kaufmann Efficient Interventional Distribution Learning in the PAC Framework
Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, Vinodchandran N. Variyam Efficient Kernelized UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard Encrypted Linear Contextual Bandit
Evrard Garcelon, Matteo Pirotta, Vianney Perchet Equivariance Discovery by Learned Parameter-Sharing
Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson, Alexander Schwing Exact Community Recovery over Signed Graphs
Xiaolu Wang, Peng Wang, Anthony Man-Cho So Fair Disaster Containment via Graph-Cut Problems
Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever Feature Screening with Kernel Knockoffs
Benjamin Poignard, Peter J. Naylor, Héctor Climente-González, Makoto Yamada Federated Functional Gradient Boosting
Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi Federated Learning with Buffered Asynchronous Aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba Finding Dynamics Preserving Adversarial Winning Tickets
Xupeng Shi, Pengfei Zheng, A. Adam Ding, Yuan Gao, Weizhong Zhang Finding Nearly Everything Within Random Binary Networks
Kartik Sreenivasan, Shashank Rajput, Jy-Yong Sohn, Dimitris Papailiopoulos Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada Flexible Accuracy for Differential Privacy
Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon Du Generalized Group Testing
Xiwei Cheng, Sidharth Jaggi, Qiaoqiao Zhou Grassmann Stein Variational Gradient Descent
Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew Duncan Heavy-Tailed Streaming Statistical Estimation
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar Hierarchical Bayesian Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh Hypergraph Simultaneous Generators
Bahman Pedrood, Carlotta Domeniconi, Kathryn Laskey Identification in Tree-Shaped Linear Structural Causal Models
Benito Van Der Zander, Marcel Wienöbst, Markus Bläser, Maciej Liskiewicz Implicitly Regularized RL with Implicit Q-Values
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist Improved Analysis of Randomized SVD for Top-Eigenvector Approximation
Ruo-Chun Tzeng, Po-An Wang, Florian Adriaens, Aristides Gionis, Chi-Jen Lu Improving Attribution Methods by Learning Submodular Functions
Piyushi Manupriya, Tarun Ram Menta, Sakethanath N. Jagarlapudi, Vineeth N. Balasubramanian Iterative Alignment Flows
Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye Jointly Efficient and Optimal Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Kwang-Sung Jun, Clement Calauzenes K-Experts - Online Policies and Fundamental Limits
Samrat Mukhopadhyay, Sourav Sahoo, Abhishek Sinha Label Differential Privacy via Clustering
Hossein Esfandiari, Vahab Mirrokni, Umar Syed, Sergei Vassilvitskii Last Layer Marginal Likelihood for Invariance Learning
Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark Van Der Wilk Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael Jordan, Ion Stoica Learning in Stochastic Monotone Games with Decision-Dependent Data
Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian Ratliff Learning Proposals for Practical Energy-Based Regression
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön Learning Sparse Fixed-Structure Gaussian Bayesian Networks
Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal Leveraging Time Irreversibility with Order-Contrastive Pre-Training
Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David Sontag Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman Marginalising over Stationary Kernels with Bayesian Quadrature
Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen Roberts Mean Nyström Embeddings for Adaptive Compressive Learning
Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco Measuring the Robustness of Gaussian Processes to Kernel Choice
William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer Deshpande, Tamara Broderick Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer Model-Agnostic Out-of-Distribution Detection Using Combined Statistical Tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Soren Hauberg, Jes Frellsen Model-Free Policy Learning with Reward Gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood Multivariate Quantile Function Forecaster
Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus Nearly Optimal Algorithms for Level Set Estimation
Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin Jamieson, Robert Nowak Neural Score Matching for High-Dimensional Causal Inference
Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris Holmes New Coresets for Projective Clustering and Applications
Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman Node Feature Kernels Increase Graph Convolutional Network Robustness
Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis Non-Separable Spatio-Temporal Graph Kernels via SPDEs
Alexander V. Nikitin, St John, Arno Solin, Samuel Kaski Nonparametric Relational Models with Superrectangulation
Masahiro Nakano, Ryo Nishikimi, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda Norm-Agnostic Linear Bandits
Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun Off-Policy Risk Assessment for Markov Decision Processes
Audrey Huang, Liu Leqi, Zachary Lipton, Kamyar Azizzadenesheli Offline Policy Selection Under Uncertainty
Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans On Coresets for Fair Regression and Individually Fair Clustering
Rachit Chhaya, Anirban Dasgupta, Jayesh Choudhari, Supratim Shit On Facility Location Problem in the Local Differential Privacy Model
Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboradi, Shi Li, Di Wang On Global-View Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds
Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Tran, Dinh Phung On PAC-Bayesian Reconstruction Guarantees for VAEs
Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj On the Assumptions of Synthetic Control Methods
Claudia Shi, Dhanya Sridhar, Vishal Misra, David Blei On the Consistency of Max-Margin Losses
Alex Nowak, Alessandro Rudi, Francis Bach On the Convergence of Continuous Constrained Optimization for Structure Learning
Ignavier Ng, Sebastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang On the Generalization of Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare On the Value of Prior in Online Learning to Rank
Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin Online Competitive Influence Maximization
Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui, Wei Chen Online Continual Adaptation with Active Self-Training
Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu Online Page Migration with ML Advice
Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrovic, Ronitt Rubinfeld Optimal Channel Selection with Discrete QCQP
Yeonwoo Jeong, Deokjae Lee, Gaon An, Changyong Son, Hyun Oh Song Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis Optimal Design of Stochastic DNA Synthesis Protocols Based on Generative Sequence Models
Eli N. Weinstein, Alan N. Amin, Will S. Grathwohl, Daniel Kassler, Jean Disset, Debora Marks Orbital MCMC
Kirill Neklyudov, Max Welling Orthogonal Multi-Manifold Enriching of Directed Networks
Ramit Sawhney, Shivam Agarwal, Atula T. Neerkaje, Kapil Jayesh Pathak PAC Mode Estimation Using PPR Martingale Confidence Sequences
Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet J. Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J. Ribeiro, Shivaram Kalyanakrishnan Pairwise Fairness for Ordinal Regression
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell Pairwise Supervision Can Provably Elicit a Decision Boundary
Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama Parallel MCMC Without Embarrassing Failures
Daniel A. De Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi Particle-Based Adversarial Local Distribution Regularization
Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Phung Physics Informed Deep Kernel Learning
Zheng Wang, Wei Xing, Robert Kirby, Shandian Zhe Point Cloud Generation with Continuous Conditioning
Larissa T. Triess, Andre Bühler, David Peter, Fabian B. Flohr, Marius Zöllner Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Sébastien M. R. Arnold, Pierre L’Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha Predictive Variational Bayesian Inference as Risk-Seeking Optimization
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama Proximal Optimal Transport Modeling of Population Dynamics
Charlotte Bunne, Laetitia Papaxanthos, Andreas Krause, Marco Cuturi Pulling Back Information Geometry
Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin, Dimitrios Kalatzis, Soren Hauberg Quadric Hypersurface Intersection for Manifold Learning in Feature Space
Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeniy Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vyalov, Anatolii Zaikovskii, Aleksandr Petiushko Random Effect Bandits
Rong Zhu, Branislav Kveton Randomized Stochastic Gradient Descent Ascent
Othmane Sebbouh, Marco Cuturi, Gabriel Peyré Reconstructing Test Labels from Noisy Loss Functions
Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier Rejection Sampling from Shape-Constrained Distributions in Sublinear Time
Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet Relational Neural Markov Random Fields
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi Resampling Base Distributions of Normalizing Flows
Vincent Stimper, Bernhard Schölkopf, Jose Miguel Hernandez-Lobato Robust Probabilistic Time Series Forecasting
Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi Second-Order Sensitivity Analysis for Bilevel Optimization
Robert Dyro, Edward Schmerling, Nikos Arechiga, Marco Pavone Spectral Pruning for Recurrent Neural Networks
Takashi Furuya, Kazuma Suetake, Koichi Taniguchi, Hiroyuki Kusumoto, Ryuji Saiin, Tomohiro Daimon Strategic Ranking
Lydia T. Liu, Nikhil Garg, Christian Borgs Super-Acceleration with Cyclical Step-Sizes
Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Lenon Minorics, Caner Turkmen, David Kernert, Patrick Bloebaum, Laurent Callot, Dominik Janzing The Fast Kernel Transform
John P. Ryan, Sebastian E. Ament, Carla P. Gomes, Anil Damle The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini Thompson Sampling with a Mixture Prior
Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta Towards Return Parity in Markov Decision Processes
Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao Transductive Robust Learning Guarantees
Omar Montasser, Steve Hanneke, Nathan Srebro Transfer Learning with Gaussian Processes for Bayesian Optimization
Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska Two-Way Sparse Network Inference for Count Data
Sijia Li, Martı́n López-Garcı́a, Neil D. Lawrence, Luisa Cutillo Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
Rickard K.A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David Sontag, Fredrik Johansson Vanishing Curvature in Randomly Initialized Deep ReLU Networks
Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurelien Lucchi Variational Continual Proxy-Anchor for Deep Metric Learning
Minyoung Kim, Ricardo Guerrero, Hai X. Pham, Vladimir Pavlovic Variational Marginal Particle Filters
Jinlin Lai, Justin Domke, Daniel Sheldon Weighted Gaussian Process Bandits for Non-Stationary Environments
Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness Shroff Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez