AISTATS 2021 Accepted Papers

• On the Effect of Auxiliary Tasks on Representation Dynamics
  Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney


• LassoNet: Neural Networks with Feature Sparsity
  Ismael Lemhadri, Feng Ruan, Rob Tibshirani


• Projection-Free Optimization on Uniformly Convex Sets
  Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta


• Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications
  Shinsaku Sakaue


• Graphical Normalizing Flows
  Antoine Wehenkel, Gilles Louppe


• One-Round Communication Efficient Distributed M-Estimation
  Yajie Bao, Weijia Xiong


• CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices
  Valerii Likhosherstov, Jared Q Davis, Krzysztof Choromanski, Adrian Weller


• Regularized Policies are Reward Robust
  Hisham Husain, Kamil Ciosek, Ryota Tomioka


• Semi-Supervised Learning with Meta-Gradient
  Taihong Xiao, Xin-Yu Zhang, Haolin Jia, Ming-Ming Cheng, Ming-Hsuan Yang


• On Information Gain and Regret Bounds in Gaussian Process Bandits
  Sattar Vakili, Kia Khezeli, Victor Picheny


• On the proliferation of support vectors in high dimensions
  Daniel Hsu, Vidya Muthukumar, Ji Xu


• Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
  Nikhil Mehta, Kevin J Liang, Vinay Kumar Verma, Lawrence Carin


• A Fast and Robust Method for Global Topological Functional Optimization
  Yitzchak E Solomon, Alexander Wagner, Paul Bendich


• Regression Discontinuity Design under Self-selection
  Sida Peng, Yang Ning


• Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns
  Ziping Xu, Amirhossein Meisami, Ambuj Tewari


• When OT meets MoM: Robust estimation of Wasserstein Distance
  Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc


• Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
  Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima


• Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability
  Naoto Ohsaka


• False Discovery Rates in Biological Networks
  Lu Yu, Tobias Kaufmann, Johannes Lederer


• Fourier Bases for Solving Permutation Puzzles
  Horace Pan, Risi Kondor


• Accelerating Metropolis-Hastings with Lightweight Inference Compilation
  Feynman Liang, Nimar S Arora, Nazanin Tehrani, Yucen Li, Michael Tingley, Erik Meijer


• Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
  Xing Han, Sambarta Dasgupta, Joydeep Ghosh


• Fully Gap-Dependent Bounds for Multinomial Logit Bandit
  Jiaqi Yang


• Alternating Direction Method of Multipliers for Quantization
  Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn


• Online Forgetting Process for Linear Regression Models
  Yuantong Li, Chi-Hua Wang, Guang Cheng


• A Bayesian nonparametric approach to count-min sketch under power-law data streams
  Emanuele Dolera, Stefano Favaro, Stefano Peluchetti


• Nonlinear Functional Output Regression: A Dictionary Approach
  Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc


• When MAML Can Adapt Fast and How to Assist When It Cannot
  Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha


• Learning Smooth and Fair Representations
  Xavier Gitiaux, Huzefa Rangwala


• On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
  Tianyi Lin, Zeyu Zheng, Elynn Chen, Marco Cuturi, Michael Jordan


• Contextual Blocking Bandits
  Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai


• Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation
  Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf


• A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling
  HaiYing Wang, Jiahui Zou


• Robust Imitation Learning from Noisy Demonstrations
  Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama


• Online Active Model Selection for Pre-trained Classifiers
  Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš, Johannes Rausch, Ce Zhang, Andreas Krause


• Online Sparse Reinforcement Learning
  Botao Hao, Tor Lattimore, Csaba Szepesvari, Mengdi Wang


• A Contraction Approach to Model-based Reinforcement Learning
  Ting-Han Fan, Peter J Ramadge


• The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry
  Tomohiro Hayase, Ryo Karakida


• Benchmarking Simulation-Based Inference
  Jan-Matthis Lueckmann, Jan F Boelts, David S Greenberg, Pedro Goncalves, Jakob H Macke


• Fisher Auto-Encoders
  Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh


• Deep Spectral Ranking
  Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis


• Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
  Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou


• On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems
  Yingjie Bi, Javad Lavaei


• Fast Learning in Reproducing Kernel Krein Spaces via Generalized Measures
  Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan Suykens


• Approximate Message Passing with Spectral Initialization for Generalized Linear Models
  Marco Mondelli, Ramji Venkataramanan


• Active Learning with Maximum Margin Sparse Gaussian Processes
  Weishi Shi, Qi Yu


• A Stein Goodness-of-test for Exponential Random Graph Models
  Wenkai Xu, Gesine D Reinert


• The Sample Complexity of Level Set Approximation
  Francois Bachoc, Tommaso R. Cesari, Sebastien Gerchinovitz


• Curriculum Learning by Optimizing Learning Dynamics
  Tianyi Zhou, Shengjie Wang, Jeff Bilmes


• Approximating Lipschitz continuous functions with GroupSort neural networks
  Ugo Tanielian, Gerard Biau


• Learning GPLVM with arbitrary kernels using the unscented transformation
  Daniel A de Souza, Diego Mesquita, João Paulo Gomes, César Lincoln C Mattos


• Low-Rank Generalized Linear Bandit Problems
  Yangyi Lu, Amirhossein Meisami, Ambuj Tewari


• On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
  Kai Brügge, Asja Fischer, Christian Igel


• Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
  Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir


• SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
  Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S Berahas, Martin Takac


• Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
  Yue Xing, Qifan Song, Guang Cheng


• On the Generalization Properties of Adversarial Training
  Yue Xing, Qifan Song, Guang Cheng


• Adversarial Robust Estimate and Risk Analysis in Linear Regression
  Yue Xing, Ruizhi Zhang, Guang Cheng


• Adaptive Approximate Policy Iteration
  Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvari


• Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
  Guillaume Ausset, Stephan Clémençon, François Portier


• Foundations of Bayesian Learning from Synthetic Data
  Harrison Wilde, Jack E Jewson, Sebastian Vollmer, Chris Holmes


• Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
  Damien Scieur, Lewis Liu, Thomas Pumir, Nicolas Boumal


• Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
  Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar


• Generalization Bounds for Stochastic Saddle Point Problems
  Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang


• Learning to Defense by Learning to Attack
  Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao


• A Deterministic Streaming Sketch for Ridge Regression
  Benwei Shi, Jeff Phillips


• Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation for Black-Box Safety-Critical Systems
  Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao


• On the role of data in PAC-Bayes
  Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy


• CADA: Communication-Adaptive Distributed Adam
  Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin


• Bandit algorithms: Letting go of logarithmic regret for statistical robustness
  Kumar Ashutosh, Jayakrishnan U Nair, Anmol Kagrecha, Krishna Jagannathan


• Geometrically Enriched Latent Spaces
  Georgios Arvanitidis, Soren Hauberg, Bernhard Schölkopf


• Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
  Ilja Kuzborskij, Claire Vernade, Andras Gyorgy, Csaba Szepesvari


• Kernel regression in high dimensions: Refined analysis beyond double descent
  Fanghui Liu, Zhenyu Liao, Johan Suykens


• Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
  Yoan Russac, Louis Faury, Olivier Cappé, Aurélien Garivier


• Logical Team Q-learning: An approach towards factored policies in cooperative MARL
  Lucas C Cassano, Ali H. Sayed


• Automatic structured variational inference
  Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven


• Neural Enhanced Belief Propagation on Factor Graphs
  Víctor Garcia Satorras, Max Welling


• Predictive Complexity Priors
  Eric T Nalisnick, Jonathan Gordon, Jose Miguel Hernandez-Lobato


• Improving predictions of Bayesian neural nets via local linearization
  Alexander Immer, Maciej Jan Korzepa, Matthias Bauer


• Generalized Spectral Clustering via Gromov-Wasserstein Learning
  Samir Chowdhury, Tom Needham


• Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
  Jiaxuan Wang, Jenna Wiens, Scott Lundberg


• Scalable Constrained Bayesian Optimization
  David Eriksson, Matthias Poloczek


• Sample efficient learning of image-based diagnostic classifiers via probabilistic labels
  Roberto I Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth, Jeevesh Kapur, Jacob Jaremko, Russell Greiner


• Nonparametric Variable Selection with Optimal Decision Stumps
  Jason M Klusowski, Peter M Tian


• Sharp Analysis of a Simple Model for Random Forests
  Jason M Klusowski


• Nested Barycentric Coordinate System as an Explicit Feature Map
  Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele


• An Analysis of the Adaptation Speed of Causal Models
  Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien


• Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
  Robin Vogel, Aurélien Bellet, Stephan Clémençon


• Efficient Computation and Analysis of Distributional Shapley Values
  Yongchan Kwon, Manuel A. Rivas, James Zou


• A constrained risk inequality for general losses
  John Duchi, Feng Ruan


• Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms
  Tengyu Xu, Yingbin Liang


• Learning Prediction Intervals for Regression: Generalization and Calibration
  Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang


• Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
  Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng


• Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
  Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang


• Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
  Zheng Wang, Wei Xing, Robert Kirby, Shandian Zhe


• Deep Fourier Kernel for Self-Attentive Point Processes
  Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie


• Robustness and scalability under heavy tails, without strong convexity
  Matthew J Holland


• Understanding the wiring evolution in differentiable neural architecture search
  Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin


• Provable Hierarchical Imitation Learning via EM
  Zhiyu Zhang, Ioannis Paschalidis


• Learning with risk-averse feedback under potentially heavy tails
  Matthew J Holland, El Mehdi Haress


• Parametric Programming Approach for More Powerful and General Lasso Selective Inference
  Vo Nguyen Le Duy, Ichiro Takeuchi


• On the High Accuracy Limitation of Adaptive Property Estimation
  Yanjun Han


• Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
  Alex M Lamb, Anirudh Goyal, Agnieszka Słowik, Michael C Mozer, Philippe Beaudoin, Yoshua Bengio


• Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
  Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei


• Interpretable Random Forests via Rule Extraction
  Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet


• Regret Minimization for Causal Inference on Large Treatment Space
  Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima


• Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions
  Shunsuke Horii


• Adaptive Sampling for Fast Constrained Maximization of Submodular Functions
  Francesco Quinzan, Vanja Doskoc, Andreas Göbel, Tobias Friedrich


• Mean-Variance Analysis in Bayesian Optimization under Uncertainty
  Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi


• Hadamard Wirtinger Flow for Sparse Phase Retrieval
  Fan Wu, Patrick Rebeschini


• Stochastic Linear Bandits Robust to Adversarial Attacks
  Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett


• ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
  Martin Royer, Frederic Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike


• Optimizing Percentile Criterion using Robust MDPs
  Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho


• On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
  Alain Durmus, Pablo Jiménez, Eric Moulines, Salem SAID


• Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy
  Onur Teymur, Jackson C Gorham, Marina Riabiz, Chris Oates


• Aligning Time Series on Incomparable Spaces
  Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth


• The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers
  Mohamed El Amine Seddik, Cosme Louart, Romain COUILLET, Mohamed Tamaazousti


• Measure Transport with Kernel Stein Discrepancy
  Matthew A Fisher, Tui Nolan, Matthew Graham, Dennis Prangle, Chris Oates


• Unifying Clustered and Non-stationary Bandits
  Chuanhao Li, Qingyun Wu, Hongning Wang


• A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
  Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier


• Transforming Gaussian Processes With Normalizing Flows
  Juan Maroñas, Oliver A Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas


• Linearly Constrained Gaussian Processes with Boundary Conditions
  Markus Lange-Hegermann


• Noise Contrastive Meta-Learning for ConditionalDensity Estimation using Kernel Mean Embeddings
  Jean-Francois Ton, Lucian CHAN, Yee Whye Teh, Dino Sejdinovic


• Top-m identification for linear bandits
  Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez


• When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
  Ziwei Guan, Tengyu Xu, Yingbin Liang


• Online k-means Clustering
  Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom


• Consistent k-Median: Simpler, Better and Robust
  Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian


• Algorithms for Fairness in Sequential Decision Making
  Min Wen, Osbert Bastani, Ufuk Topcu


• On Learning Continuous Pairwise Markov Random Fields
  Abhin Shah, Devavrat Shah, Gregory W Wornell


• Abstract Value Iteration for Hierarchical Reinforcement Learning
  Kishor Jothimurugan, Osbert Bastani, Rajeev Alur


• Differentially Private Analysis on Graph Streams
  Jalaj Upadhyay, Sarvagya Upadhyay, Raman Arora


• Learning with Hyperspherical Uniformity
  Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller


• Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function
  Ioannis Tsaknakis, Mingyi Hong


• Latent Derivative Bayesian Last Layer Networks
  Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters


• Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
  Nhuong Van Nguyen, Toan N Nguyen, PHUONG HA NGUYEN, Quoc Tran-Dinh, Lam M Nguyen, Marten van Dijk


• Provably Safe PAC-MDP Exploration Using Analogies
  Melrose Roderick, Vaishnavh Nagarajan, Zico Kolter


• Maximal Couplings of the Metropolis-Hastings Algorithm
  Guanyang Wang, John O'Leary, Pierre E Jacob


• Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
  Yahav Bechavod, Katrina Ligett, Steven Wu, Juba Ziani


• Goodness-of-Fit Test of Mismatched Models for Self-Exciting Processes
  Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie


• Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship
  Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman


• A Study of Condition Numbers for First-Order Optimization
  Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas


• Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
  Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar


• Differentially Private Online Submodular Maximization
  Sebastian Perez Salazar, Rachel Cummings


• Anderson acceleration of coordinate descent
  Quentin Bertrand, Mathurin Massias


• Inference in Stochastic Epidemic Models via Multinomial Approximations
  Nick Whiteley, Lorenzo Rimella


• Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
  Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien


• SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
  Robert M Gower, Othmane Sebbouh, Nicolas Loizou


• Stable ResNet
  Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau


• Latent variable modeling with random features
  Gregory Gundersen, Michael Zhang, Barbara Engelhardt


• Reaping the Benefits of Bundling under High Production Costs
  Will Ma, David Simchi-Levi


• Momentum Improves Optimization on Riemannian Manifolds
  Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi


• Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
  Alan Kuhnle


• On Data Efficiency of Meta-learning for Personalized Federated Learning
  Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar


• Hyperparameter Transfer Learning with Adaptive Complexity
  Samuel Horváth, Aaron Klein, Peter Richtarik, Cedric Archambeau


• Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency
  Yuyang Deng, Mehrdad Mahdavi


• Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
  Avishek Ghosh, Abishek Sankararaman, Ramchandran Kannan


• On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
  Jeongyeol Kwon, Nhat Ho, Constantine Caramanis


• Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification
  Zhuo Sun, Jijie Wu, Xiaoxu Li, Wenming Yang, Jing-Hao Xue


• Tractable contextual bandits beyond realizability
  Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey


• Learning User Preferences in Non-Stationary Environments
  Wasim Huleihel, Soumyabrata Pal, Ofer Shayevitz


• Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
  Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas, xiao wang


• Efficient Statistics for Sparse Graphical Models from Truncated Samples
  Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas


• Have We Learned to Explain?
  Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath


• Feedback Coding for Active Learning
  Gregory H Canal, Matthieu Bloch, Christopher J Rozell


• Shadow Manifold Hamiltonian Monte Carlo
  Chris van der Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese


• Towards Understanding the Optimal Behaviors of Deep Active Learning Algorithms
  Yilun Zhou, Adi Renduchintala, Xian Li, Sida I Wang, Yashar Mehdad, Asish Ghoshal


• Identification of Matrix Joint Block Diagonalization
  Yunfeng Cai, Ping Li


• Understanding Gradient Clipping In Incremental Gradient Methods
  Jiang Qian, Yuren Wu, Bojin Zhuang, Shaojun Wang, Jing Xiao


• A Variational Information Bottleneck Approach to Multi-Omics Data Integration
  Changhee Lee, Mihaela van der Schaar


• On the Privacy Properties of GAN-generated Samples
  Zinan Lin, Vyas Sekar, Giulia Fanti


• Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
  Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel Riek, Kamalika Chaudhuri


• Learning Complexity of Simulated Annealing
  Avrim Blum, Chen Dan, Saeed Seddighin


• Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
  Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvarinen


• Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers
  Lunjia Hu, Omer Reingold


• Uniform Convergence in Offline Policy Evaluation and Learning for Reinforcement Learning
  Ming Yin, Yu Bai, Yu-Xiang Wang


• Q$Q$-learning with Logarithmic Regret
  Kunhe Yang, Lin Yang, Simon Du


• An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
  QIN DING, Cho-Jui Hsieh, James Sharpnack


• Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization
  Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhiquan Luo


• Robust and Private Learning of Halfspaces
  Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen


• Minimax Model Learning
  Cameron Voloshin, Nan Jiang, Yisong Yue


• On the Faster Alternating Least-Squares for CCA
  Zhiqiang Xu, Ping Li


• Exploiting Equality Constraints in Causal Inference
  Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl


• Collaborative Classification from Noisy Labels
  Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas


• Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation
  Han Bao, Masashi Sugiyama


• Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT
  Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian


• Why did the distribution change?
  Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng


• Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers
  Hadi Mohasel Afshar, Rafael Oliveira, Sally Cripps


• Iterative regularization for convex regularizers
  Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa


• Competing AI: How does competition feedback affect machine learning?
  Tony Ginart, Eva Y Zhang, Yongchan Kwon, James Zou


• Stability and Risk Bounds of Iterative Hard Thresholding
  Xiaotong Yuan, Ping Li


• Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation
  Yuki Ohnishi, Jean Honorio


• On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
  Youssef Mroueh, Truyen V. Nguyen


• Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
  Qiming Du, Gérard Biau, Francois Petit, Raphaël Porcher


• Improved Complexity Bounds in Wasserstein Barycenter Problem
  Darina Dvinskikh, Daniil Tiapkin


• Sparse Algorithms for Markovian Gaussian Processes
  William Wilkinson, Arno Solin, Vincent Adam


• Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
  Lisa Schut, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, medb corcoran, Yarin Gal


• Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
  Nina Vesseron, Ievgen Redko, Charlotte Laclau


• All of the Fairness for Edge Prediction with Optimal Transport
  Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron


• γ-ABC: Outlier-Robust Approximate Bayesian Computation based on A Robust Divergence Estimator
  Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama


• Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
  Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B Grosse, Joern-Henrik Jacobsen


• Online probabilistic label trees
  Marek Wydmuch, Kalina Jasinska, Devanathan Thiruvenkatachari, Krzysztof Dembczynski


• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
  Alicia Curth, Mihaela van der Schaar


• DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation
  Frederik Harder, Kamil Adamczewski, Mijung Park


• Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
  Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, Wipf David


• Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
  Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone


• Free-rider Attacks on Model Aggregation in Federated Learning
  Yann Fraboni, Richard Vidal, Marco Lorenzi


• Reinforcement Learning in Parametric MDPs with Exponential Families
  Sayak Ray Chowdhury, Aditya Gopalan, Odalric Maillard


• Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
  Mike Laszkiewicz, Johannes Lederer, Asja Fischer


• No-regret Algorithms for Multi-task Bayesian Optimization
  Sayak Ray Chowdhury, Aditya Gopalan


• Explicit Regularization of Stochastic Gradient Methods through Duality
  Anant Raj, Francis Bach


• Towards Understanding the Implicit Bias of the Noise in Nonconvex Matrix Factorization
  Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao


• CONTRA: Contrarian statistics for controlled variable selection
  Mukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath


• Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
  Kai Cui, Heinz Koeppl


• Moment-Based Variational Inference for Stochastic Differential Equations
  Christian Wildner, Heinz Koeppl


• PClean: Bayesian Data Cleaning at Scale via Domain-Specific Probabilistic Programming
  Alexander K. Lew, Monica N Agrawal, David Sontag, Vikash Mansinghka


• Adaptive wavelet pooling for convolutional neural networks
  Moritz Wolter, Jochen Garcke


• The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
  Fergus Simpson, Alexis Boukouvalas, Vaclav Cadek, Elvijs Sarkans, Nicolas Durrande


• Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
  Shingo Yashima, Atsushi Nitanda, Taiji Suzuki


• High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
  Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard


• Counterfactual Representation Learning with Balancing Weights
  Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin


• Gradient Descent in RKHS with Importance Labeling
  Tomoya Murata, Taiji Suzuki


• Learning with Gradient Descent and Weakly Convex Losses
  Dominic Richards, Mike Rabbat


• Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
  Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi


• Approximate Data Deletion from Machine Learning Models
  Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou


• Budgeted and Non-budgeted Causal Bandits
  Vineet Nair, Vishakha Patil, Gaurav Sinha


• Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss
  Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying


• Equitable and Optimal Transport with Multiple Agents
  Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi


• A Variational Inference Approach to Learning Multivariate Wold Processes
  William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran


• Active Online Learning with Hidden Shifting Domains
  Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang


• No-Regret Algorithms for Private Gaussian Process Bandit Optimization
  Abhimanyu Dubey


• The Teaching Dimension of Kernel Perceptron
  Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen


• Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
  Sandesh M Adhikary, Siddarth Srinivasan, Jacob E Miller, Guillaume Rabusseau, Byron Boots


• Localizing Changes in High-Dimensional Regression Models
  Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu


• On the Suboptimality of Negative Momentum for Minimax Optimization
  Guodong Zhang, Yuanhao Wang


• Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
  Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe


• Corralling Stochastic Bandit Algorithms
  Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri


• Context-Specific Likelihood Weighting
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• Learning Matching Representations for Individualized Organ Transplantation Allocation
  Can Xu, Ahmed Alaa, Ioana Bica, Brent Ershoff, Maxime Cannesson, Mihaela van der Schaar


• Stochastic Gradient Descent Meets Distribution Regression
  Nicole Muecke


• Learning Contact Dynamics using Physically Structured Neural Networks
  Andreas Hochlehnert, Alexander Terenin, Steindor Saemundsson, Marc Deisenroth


• Robustness to Misspecification Via Improper Learning
  Annie Marsden, John Duchi, Gregory Valiant


• Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization
  Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu


• Selective Classification via One-Sided Prediction
  Aditya Gangrade, Anil Kag, Venkatesh Saligrama


• Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning
  Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu


• vqSGD: Vector Quantized Stochastic Gradient Descent
  Venkata Gandikota, Daniel M Kane, Raj Kumar Maity, Arya Mazumdar


• Smooth Bandit Optimization: Generalization to Holder Space
  Yusha Liu, Yining Wang, Aarti Singh


• List Learning with Attribute Noise
  Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie


• High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
  Kristjan Greenewald, Karthikeyan Shanmugam, Dmitriy Katz


• Fractional moment-preserving initialization schemes for trainingfully-connected neural networks
  Mert Gurbuzbalaban, Yuanhan Hu


• Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach
  Yves-Laurent Kom Samo


• Federated f-Differential Privacy
  Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su


• Clustering multilayer graphs with missing nodes
  Guillaume Braun, Hemant Tyagi, Christophe Biernacki


• Implicit Regularization in Deep Learning: A View from Function Space
  Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien


• Aggregating Incomplete and Noisy Rankings
  Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos


• Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
  Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos


• Optimal query complexity for private sequential learning against eavesdropping
  Jiaming Xu, Kuang Xu, Dana Yang


• Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent
  Suriya Gunasekar, Blake E Woodworth, Nathan Srebro


• Differentiable Causal Discovery Under Unmeasured Confounding
  Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser


• The Sample Complexity of Meta Sparse Regression
  Zhanyu Wang, Jean Honorio


• A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
  Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu


• Group testing for connected communities
  Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo, Christina Fragouli, Suhas Diggavi


• Federated Learning with Compression: Unified Analysis and Sharp Guarantees
  Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi


• Variational Autoencoder with Learned Latent Structure
  Marissa Connor, Gregory H Canal, Christopher J Rozell


• RankDistil: Knowledge Distillation for Ranking
  Sashank Reddi, Rama Kumar Pasumarthi, Aditya K Menon, Ankit Singh Rawat, Felix Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar


• Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
  Yu Gong, Hossein Hajimirsadeghi, Jiawei He, Thibaut Durand, Greg Mori


• On Linear Convergence of Policy Gradient Methods for Finite MDPs
  Jalaj Bhandari, Daniel Russo


• Minimal enumeration of all possible total effects in a Markov equivalence class
  Richard F Guo, Emilija Perkovic


• Differentially Private Weighted Sampling
  Edith Cohen, Ofir Geri, Tamas Sarlos, Uri Stemmer


• Learning the Truth From Only One Side of the Story
  Heinrich Jiang, Qijia Jiang, Aldo Pacchiano


• Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples
  Yixing Zhang, Xiuyuan Cheng, Galen Reeves


• Bayesian Inference with Certifiable Adversarial Robustness
  Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang, Marta Kwiatkowska


• Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors
  Benjamin Moseley, Sergei Vassilvtiskii, Yuyan Wang


• Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference
  Sean C Plummer, Shuang Zhou, Anirban Bhattacharya, David Dunson, Debdeep Pati


• Reinforcement Learning for Mean Field Games with Strategic Complementarities
  Kiyeob Lee, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai


• Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
  Sebastian Macaluso, Craig S Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum


• Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
  Chaoqi Wang, Shengyang Sun, Roger B Grosse


• On the Consistency of Metric and Non-Metric K-Medoids
  He Jiang, Ery Arias-Castro


• Piecewise-Stationary Off-Policy Optimization
  Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed


• Efficient Interpolation of Density Estimators
  Paxton M Turner, Jingbo Liu, Philippe Rigollet


• A Statistical Perspective on Coreset Density Estimation
  Paxton M Turner, Jingbo Liu, Philippe Rigollet


• Shuffled Model of Differential Privacy in Federated Learning
  ANTONIOUS M. Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, Ananda Theertha Suresh


• CLAR: Contrastive Learning of Auditory Representations
  Haider Al-Tahan, Yalda Mohsenzadeh


• Designing Transportable Experiments
  My Phan, David Arbour, Drew Dimmery, Anup Rao


• A Minimax Theorem for Nonconcave-Nonconvex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
  Gauthier Gidel, David Balduzzi, Wojciech M Czarnecki, Marta Garnelo, Yoram Bachrach


• Private optimization without constraint violations
  andres munoz, Umar Syed, Sergei Vassilvtiskii, Ellen Vitercik


• Direct Loss Minimization for Sparse Gaussian Processes
  Yadi Wei, Rishit Sheth, Roni Khardon


• Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
  Zachary Charles, Jakub Konečný


• Linear Models are Robust Optimal Under Strategic Behavior
  Wei Tang, Chien-Ju Ho, Yang Liu


• Matern Gaussian Processes on Graphs
  Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, Nicolas Durrande


• Minimax optimality of Laplacian smoothing
  Alden J Green, Sivaraman Balakrishnan, Ryan Tibshirani


• Evaluating Model Robustness to Dataset Shift
  Adarsh Subbaswamy, Roy Adams, Suchi Saria


• Continuum-Armed Bandits: A Function Space Perspective
  Shashank Singh


• Regret-Optimal Filtering
  Oron Sabag, Babak Hassibi


• Evading the Curse of Dimensionality in Unconstrained Private GLMs
  Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta


• One Sketch for All: Non-linear Random Features from Compressed Linear Measurements
  Xiaoyun Li, Ping Li


• Reinforcement Learning for Constrained and Multi-Objective Markov Decision Processes
  Ather Gattami, Qinbo Bai, Vaneet Aggarwal


• Principal Component Regression with Semirandom Observations via Matrix Completion
  Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena


• Ridge Regression with Overparametrized Networks Converge to Ridgelet Spectrum
  Sho Sonoda, Isao Ishikawa, Masahiro Ikeda


• Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
  Shengjia Zhao, Stefano Ermon


• Credible Sample Elicitation
  Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang


• Random Coordinate Underdamped Langevin Monte Carlo
  Zhiyan Ding, Qin Li, Jianfeng Lu, Stephen J Wright


• Influence Decompositions For Neural Network Attribution
  Kyle Reing, Greg Ver Steeg, Aram Galstyan


• Variational inference for nonlinear ordinary differential equations
  Sanmitra Ghosh, Paul Birrell, Daniela De Angelis


• Approximation Algorithms for Orthogonal Non-negative Matrix Factorization
  Moses Charikar, Lunjia Hu


• Fast Adaptation with Linearized Neural Networks
  Wesley J Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Gordon Wilson, Andreas Damianou


• Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization
  Jelena Diakonikolas, Constantinos Daskalakis, Michael Jordan


• A Change of Variables Method For Rectangular Matrix-Vector Products
  Edmond Cunningham, Madalina Fiterau


• Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case
  Yufeng Zhang, Zhuoran Yang, Zhaoran Wang


• Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
  Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis


• Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
  Jacky Y Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo


• Causal Inference with Selectively Deconfounded Data
  Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur


• Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model
  Libby Zhang, Tim Dunn, Jesse D Marshall, Bence Olveczky, Scott Linderman


• Mirror Descent View for Neural Network Quantization
  Thalaiyasingam Ajanthan, Kartik Gupta, Philip Torr, RICHARD HARTLEY, Puneet Dokania


• Power of Hints for Online Learning with Movement Costs
  Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit


• Stochastic Bandits with Linear Constraints
  Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang


• Significance of Gradient Information in Bayesian Optimization
  Shubhanshu Shekhar, Tara Javidi


• Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
  Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou


• DAG-structured Clustering by Nearest-Neighbors
  Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum


• Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
  Yunhao Tang, Alp Kucukelbir


• Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience
  Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan


• Location Trace Privacy Under Conditional Priors
  Casey Meehan, Kamalika Chaudhuri


• Large Scale K-Median Clustering for Stable Clustering Instances
  Konstantin Voevodski


• An Optimal Reduction of TV-Denoising to Adaptive Online Learning
  Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang


• Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints
  Omid Sadeghi, Maryam Fazel


• Federated Multi-armed Bandits with Personalization
  Chengshuai Shi, Cong Shen, Jing Yang


• Hierachical Inducing Point Gaussian Process for Inter-domian Observations
  Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, John Cunningham


• Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
  Yifan Chen, Yun Yang


• Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms
  Tianyu Ding, Zhihui Zhu, Manolis Tsakiris, Rene Vidal, Daniel P Robinson


• Accumulations of Projections—A Unified Framework for Random Sketches in Kernel Ridge Regression
  Yifan Chen, Yun Yang


• Robust hypothesis testing and distribution estimation in Hellinger distance
  Ananda Theertha Suresh


• Faster Kernel Interpolation for Gaussian Processes
  Mohit Yadav, Daniel Sheldon, Cameron Musco


• SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
  Hyun-Suk Lee, Cong Shen, William Zame, Jang-Won Lee, Mihaela van der Schaar


• Detection and Defense of Topological Adversarial Attacks on Graphs
  Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif H Khan, Liheng Ma, Mark Coates


• Training a Single Bandit Arm
  Eren Ozbay, Vijay Kamble


• Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
  Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo, Rahul Jain


• Multi-armed bandits with cost subsidy
  Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula


• Causal Modeling with Stochastic Confounders
  Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze Yun Leong


• The Multiple Instance Learning Gaussian Process Probit Model
  Fulton Wang, Ali Pinar


• Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances
  Hunter Lang, Aravind Reddy, David Sontag, Aravindan Vijayaraghavan


• Meta Learning in the Continuous Time Limit
  Ruitu Xu, Lin Chen, Amin Karbasi


• Fast and Smooth Interpolation on Wasserstein Space
  Julien Clancy, Philippe Rigollet, Sinho Chewi, Thibaut Le Gouic, Austin Stromme, George Stepaniants


• Efficient Balanced Treatment Assignments for Experimentation
  David Arbour, Drew Dimmery, Anup Rao


• Tensor Networks for Probabilistic Sequence Modeling
  Jacob E Miller, Guillaume Rabusseau, John Terilla


• Experimental Design for Regret Minimization in Linear Bandits
  Andrew J Wagenmaker, Julian Katz-Samuels, Kevin Jamieson


• DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks
  Shiyun Xu, Zhiqi Bu


• Dynamic Cutset Networks
  Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate


• Associative Convolutional Layers
  Hamed Omidvar, Vahideh Akhlaghi, Hao Su, Massimo Franceschetti, Rajesh Gupta


• Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
  Jeremiah Z Liu


• Kernel Interpolation for Scalable Online Gaussian Processes
  Samuel Stanton, Wesley J Maddox, Ian A Delbridge, Andrew Gordon Gordon Wilson


• Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
  Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li


• Contrastive learning of strong-mixing continuous-time stochastic processes
  Bingbin Liu, Pradeep Ravikumar, Andrej Risteski


• TenIPS: Inverse Propensity Sampling for Tensor Completion
  Chengrun Yang, Lijun Ding, Ziyang Wu, Madeleine Udell


• Sketch based Memory for Neural Networks
  Rina Panigrahy, Xin Wang, Manzil Zaheer


• Uniform consistency of cross-validation estimators for high-dimensional ridge regression
  Pratik Patil, Yuting Wei, Alessandro Rinaldo, Ryan Tibshirani


• A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
  Zhiqi Bu, Shiyun Xu, Kan Chen


• Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees
  Alessio Mazzetto, Dylan Sam, Andrew H Park, Eli Upfal, Stephen H Bach


• Principal Subspace Estimation Under Information Diffusion
  Fan Zhou, Ping Li, Zhixin Zhou


• A Hybrid Approximation to the Marginal Likelihood
  Eric Chuu, Debdeep Pati, Anirban Bhattacharya


• Prediction with Finitely many Errors Almost Surely
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• Density of States Estimation for Out of Distribution Detection
  Warren R Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua V Dillon


• Product Manifold Learning
  Sharon Zhang, Amit Moscovich, Amit Singer


• Automatic Differentiation Variational Inference with Mixtures
  Warren R Morningstar, Sharad Vikram, Cusuh Ham, Andrew Gallagher, Joshua V Dillon


• Fair for All: Best-effort Fairness Guarantees for Classification
  Anilesh K Krishnaswamy, Zhihao Jiang, Kangning Wang, Yu Cheng, Kamesh Munagala


• Taming heavy-tailed features by shrinkage
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• Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension
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• Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
  Mayee Chen, Benjamin Cohen-Wang, Stephen O Mussmann, Frederic Sala, Christopher Re


• Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
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• Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
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• Understanding Robustness in Teacher-Student Setting: A New Perspective
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• Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences
  Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld


• Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning
  Zhengqing Zhou, Qinxun Bai, Zhengyuan Zhou, Linhai Qiu, Jose Blanchet, Peter Glynn


• Online Model Selection for Reinforcement Learning with Function Approximation
  Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill


• Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
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• Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
  Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela


• A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits
  Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi


• Good Classifiers are Abundant in the Interpolating Regime
  Ryan Theisen, Jason M Klusowski, Michael Mahoney


• No-Regret Reinforcement Learning with Heavy-Tailed Rewards
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• A Spectral Analysis of Dot-product Kernels
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• Towards Flexible Device Participation in Federated Learning
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• Active Learning under Label Shift
  Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue


• Tracking Regret Bounds for Online Submodular Optimization
  Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka


• Offline detection of change-points in the mean for stationary graph signals.
  Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos


• Hyperbolic graph embedding with enhanced semi-implicit variational inference.
  Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan I Tamir


• Rao-Blackwellised parallel MCMC
  Tobias Schwedes, Ben Calderhead


• Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression
  Ian Covert, Su-In Lee


• Calibrated Adaptive Probabilistic ODE Solvers
  Nathanael Bosch, Philipp Hennig, Filip Tronarp


• Online Learning of Nonlinear Control with Guaranteed Success: An Oracle-Based, Robust Control-Powered Approach
  Dimitar M Ho, Hoang M. Le, John Doyle, Yisong Yue


• Probabilistic Sequential Matrix Factorization
  Omer Deniz Akyildiz, Gerrit JJ van den Burg, Theodoros Damoulas, Mark Steel


• An Analysis of LIME for Text Data
  Dina DM Mardaoui, Damien Garreau


• Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information
  Prathamesh Mayekar, Ananda Theertha Suresh, Himanshu Tyagi


• Scalable Gaussian Process Variational Autoencoders
  Metod Jazbec, Vincent Fortuin, Michael A L Pearce, Stephan Mandt, Gunnar Rätsch


• Causal Autoregressive Flows
  Ilyes Khemakhem, Ricardo Monti, Robert Leech, Aapo Hyvarinen


• Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models
  Jan Achterhold, Joerg Stueckler


• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
  Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko


• Spectral Tensor Train Parameterization of Deep Learning Layers
  Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool


• Local SGD: Unified Theory and New Efficient Methods
  Eduard Gorbunov, Filip Hanzely, Peter Richtarik


• Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
  Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth


• Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
  Vikas K Garg, Adam Tauman Kalai, Katrina Ligett, Steven Wu


• The Base Measure Problem and its Solution
  Alexey Radul, Boris Alexeev


• Revisiting Projection-free Online Learning: the Strongly Convex Case
  Ben Kretzu, Dan Garber


• A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models
  Samet Oymak, Talha Cihad Gulcu


• Logistic Q-Learning
  Joan Bas Serrano, Sebastian Curi, Andreas Krause, Gergely Neu


• Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
  Nafiseh Ghoroghchian, Gautam Dasarathy, Stark Draper


• Sequential Random Sampling Revisited: Hidden Shuffle Method
  Michael Shekelyan, Graham Cormode


• Dirichlet Pruning for Convolutional Neural Networks
  Kamil Adamczewski, Mijung Park


• Couplings for Multinomial Hamiltonian Monte Carlo
  Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge


• Learning Bijective Feature Maps for Linear ICA
  Alexander Camuto, Matthew Willetts, Chris Holmes, Brooks Paige, Stephen Roberts


• Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
  Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato


• One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks
  Hanjing Zhu, Jiaming Xu


• On the Memory Mechanism of Tensor-Power Recurrent Models
  Hejia Qiu, Chao Li, Ying Weng, Zhun Sun, Xingyu He, Qibin Zhao


• Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
  Marc Abeille, Louis Faury, Clement Calauzenes


• Causal Inference under Networked Interference and Intervention Policy Enhancement
  Yunpu Ma, Volker Tresp


• GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
  Mucong Ding, Constantinos Daskalakis, Soheil Feizi


• Latent Gaussian process with composite likelihoods and numerical quadrature
  Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki


• Deep Generative Pattern-Set Mixture Models
  Sahra Ghalebikesabi, Rob Cornish, Chris Holmes, Luke Kelly


• Minimax Estimation of Laplacian Constrained Precision Matrices
  Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel Palomar


• A Stochastic Approximate Gradient Ascent Method for Bayesian Experimental Design with Implicit Models
  Jiaxin Zhang, Sirui Bi, Guannan Zhang


• Distribution Regression for Sequential Data
  Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J Lyons


• Completing the Picture: Randomized Smoothing Suffers from Curse of Dimensionality for a Large Family of Distributions
  Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann


• Direct-Search Methods for a Class of Non-Convex Min-Max Games
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• Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
  Qadeer Ahmed Khan, Patrick Wenzel, Daniel Cremers


• Learning Temporal Point Processes with Intermittent Observations
  Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De


• On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity
  Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier


• Robust Learning under Strong Noise via SQs
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