AISTATS 2021
455 papers
CADA: Communication-Adaptive Distributed Adam Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering Sebastian Macaluso, Craig Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum Consistent K-Median: Simpler, Better and Robust Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian CONTRA: Contrarian Statistics for Controlled Variable Selection Mukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath Does Invariant Risk Minimization Capture Invariance? Pritish Kamath, Akilesh Tangella, Danica Sutherland, Nathan Srebro Fair for All: Best-Effort Fairness Guarantees for Classification Anilesh Krishnaswamy, Zhihao Jiang, Kangning Wang, Yu Cheng, Kamesh Munagala Federated Learning with Compression: Unified Analysis and Sharp Guarantees Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi 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 Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes Nhuong Nguyen, Toan Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam Nguyen, Marten Dijk Nonlinear Functional Output Regression: A Dictionary Approach Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc Quantum Tensor Networks, Stochastic Processes, and Weighted Automata Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots RankDistil: Knowledge Distillation for Ranking Sashank Reddi, Rama Kumar Pasumarthi, Aditya Menon, Ankit Singh Rawat, Felix Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert Berahas, Martin Takac The Minecraft Kernel: Modelling Correlated Gaussian Processes in the Fourier Domain Fergus Simpson, Alexis Boukouvalas, Vaclav Cadek, Elvijs Sarkans, Nicolas Durrande Understanding Robustness in Teacher-Student Setting: A New Perspective Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian vqSGD: Vector Quantized Stochastic Gradient Descent Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar When OT Meets MoM: Robust Estimation of Wasserstein Distance Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d’Alché-Buc Why Did the Distribution Change? Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits
Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi A Study of Condition Numbers for First-Order Optimization
Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas A Theoretical Analysis of Catastrophic Forgetting Through the NTK Overlap Matrix
Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier A Variational Inference Approach to Learning Multivariate Wold Processes
Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Feynman Liang, Nimar Arora, Nazanin Tehrani, Yucen Li, Michael Tingley, Erik Meijer Active Learning Under Label Shift
Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue Active Online Learning with Hidden Shifting Domains
Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang Adaptive Approximate Policy Iteration
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvari Aggregating Incomplete and Noisy Rankings
Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos Aligning Time Series on Incomparable Spaces
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth All of the Fairness for Edge Prediction with Optimal Transport
Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron Alternating Direction Method of Multipliers for Quantization
Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou Associative Convolutional Layers
Hamed Omidvar, Vahideh Akhlaghi, Hao Su, Massimo Franceschetti, Rajesh Gupta Automatic Differentiation Variational Inference with Mixtures
Warren Morningstar, Sharad Vikram, Cusuh Ham, Andrew Gallagher, Joshua Dillon Automatic Structured Variational Inference
Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel Gerven Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis J. Alexander, Xiaoning Qian Bayesian Inference with Certifiable Adversarial Robustness
Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang, Marta Kwiatkowska Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, Jakob Macke Budgeted and Non-Budgeted Causal Bandits
Vineet Nair, Vishakha Patil, Gaurav Sinha Calibrated Adaptive Probabilistic ODE Solvers
Nathanael Bosch, Philipp Hennig, Filip Tronarp Causal Autoregressive Flows
Ilyes Khemakhem, Ricardo Monti, Robert Leech, Aapo Hyvarinen Causal Inference with Selectively Deconfounded Data
Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur Causal Modeling with Stochastic Confounders
Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze Yun Leong Clustering Multilayer Graphs with Missing Nodes
Guillaume Braun, Hemant Tyagi, Christophe Biernacki Collaborative Classification from Noisy Labels
Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas Contextual Blocking Bandits
Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai Convergence Properties of Stochastic Hypergradients
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo Corralling Stochastic Bandit Algorithms
Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri Counterfactual Representation Learning with Balancing Weights
Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin Couplings for Multinomial Hamiltonian Monte Carlo
Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge DAG-Structured Clustering by Nearest Neighbors
Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum Deep Generative Missingness Pattern-Set Mixture Models
Sahra Ghalebikesabi, Rob Cornish, Chris Holmes, Luke Kelly Deep Spectral Ranking
Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis Density of States Estimation for Out of Distribution Detection
Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua Dillon Detection and Defense of Topological Adversarial Attacks on Graphs
Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif Khan, Liheng Ma, Mark Coates Differentiable Causal Discovery Under Unmeasured Confounding
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser Differentiable Divergences Between Time Series
Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert Differentially Private Weighted Sampling
Edith Cohen, Ofir Geri, Tamas Sarlos, Uri Stemmer Direct-Search for a Class of Stochastic Min-Max Problems
Sotirios-Konstantinos Anagnostidis, Aurelien Lucchi, Youssef Diouane Distribution Regression for Sequential Data
Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin Bonilla, Terry Lyons Dynamic Cutset Networks
Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate Efficient Interpolation of Density Estimators
Paxton Turner, Jingbo Liu, Philippe Rigollet Efficient Statistics for Sparse Graphical Models from Truncated Samples
Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas Equitable and Optimal Transport with Multiple Agents
Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi Exploiting Equality Constraints in Causal Inference
Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl Fast Adaptation with Linearized Neural Networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson, Andreas Damianou Fast and Smooth Interpolation on Wasserstein Space
Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin Stromme Federated F-Differential Privacy
Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su Feedback Coding for Active Learning
Gregory Canal, Matthieu Bloch, Christopher Rozell Fisher Auto-Encoders
Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh Foundations of Bayesian Learning from Synthetic Data
Harrison Wilde, Jack Jewson, Sebastian Vollmer, Chris Holmes Geometrically Enriched Latent Spaces
Georgios Arvanitidis, Soren Hauberg, Bernhard Schölkopf Graphical Normalizing Flows
Antoine Wehenkel, Gilles Louppe Group Testing for Connected Communities
Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo, Christina Fragouli, Suhas Diggavi Hidden Cost of Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar Hierarchical Inducing Point Gaussian Process for Inter-Domian Observations
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, John Cunningham Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference.
Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan Tamir Hyperparameter Transfer Learning with Adaptive Complexity
Samuel Horváth, Aaron Klein, Peter Richtarik, Cedric Archambeau Implicit Regularization via Neural Feature Alignment
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien Improved Exploration in Factored Average-Reward MDPs
Mohammad Sadegh Talebi, Anders Jonsson, Odalric Maillard Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien Veiga, Erwan Scornet Iterative Regularization for Convex Regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa Kernel Interpolation for Scalable Online Gaussian Processes
Samuel Stanton, Wesley Maddox, Ian Delbridge, Andrew Gordon Wilson Latent Derivative Bayesian Last Layer Networks
Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters Latent Variable Modeling with Random Features
Gregory Gundersen, Michael Zhang, Barbara Engelhardt Learning Bijective Feature Maps for Linear ICA
Alexander Camuto, Matthew Willetts, Chris Holmes, Brooks Paige, Stephen Roberts Learning Contact Dynamics Using Physically Structured Neural Networks
Andreas Hochlehnert, Alexander Terenin, Steindor Saemundsson, Marc Deisenroth Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir Learning Temporal Point Processes with Intermittent Observations
Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De Learning to Defend by Learning to Attack
Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao Learning with Hyperspherical Uniformity
Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller List Learning with Attribute Noise
Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie Localizing Changes in High-Dimensional Regression Models
Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu Logistic Q-Learning
Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu Longitudinal Variational Autoencoder
Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki Low-Rank Generalized Linear Bandit Problems
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari Matérn Gaussian Processes on Graphs
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, Nicolas Durrande Measure Transport with Kernel Stein Discrepancy
Matthew Fisher, Tui Nolan, Matthew Graham, Dennis Prangle, Chris Oates Meta-Learning Divergences for Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang Minimax Model Learning
Cameron Voloshin, Nan Jiang, Yisong Yue Mirror Descent View for Neural Network Quantization
Thalaiyasingam Ajanthan, Kartik Gupta, Philip Torr, Richad Hartley, Puneet Dokania Model Updating After Interventions Paradoxically Introduces Bias
James Liley, Samuel Emerson, Bilal Mateen, Catalina Vallejos, Louis Aslett, Sebastian Vollmer Momentum Improves Optimization on Riemannian Manifolds
Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi Multi-Armed Bandits with Cost Subsidy
Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel Riek, Kamalika Chaudhuri Nested Barycentric Coordinate System as an Explicit Feature mAP
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele Non-Stationary Off-Policy Optimization
Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed On Data Efficiency of Meta-Learning
Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar On the Importance of Hyperparameter Optimization for Model-Based Reinforcement Learning
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra On the Memory Mechanism of Tensor-Power Recurrent Models
Hejia Qiu, Chao Li, Ying Weng, Zhun Sun, Xingyu He, Qibin Zhao On the Role of Data in PAC-Bayes Bounds
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel Roy Online Active Model Selection for Pre-Trained Classifiers
Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš, Johannes Rausch, Ce Zhang, Andreas Krause Online K-Means Clustering
Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom Online Probabilistic Label Trees
Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczynski Online Sparse Reinforcement Learning
Botao Hao, Tor Lattimore, Csaba Szepesvari, Mengdi Wang Optimizing Percentile Criterion Using Robust MDPs
Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho Power of Hints for Online Learning with Movement Costs
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit Predictive Complexity Priors
Eric Nalisnick, Jonathan Gordon, Jose Miguel Hernandez-Lobato Private Optimization Without Constraint Violations
Andres Munoz, Umar Syed, Sergei Vassilvtiskii, Ellen Vitercik Probabilistic Sequential Matrix Factorization
Omer Deniz Akyildiz, Gerrit Burg, Theodoros Damoulas, Mark Steel Product Manifold Learning
Sharon Zhang, Amit Moscovich, Amit Singer Projection-Free Optimization on Uniformly Convex Sets
Thomas Kerdreux, Alexandre d’Aspremont, Sebastian Pokutta Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo Jovanovic Provably Safe PAC-MDP Exploration Using Analogies
Melrose Roderick, Vaishnavh Nagarajan, Zico Kolter Rao-Blackwellised Parallel MCMC
Tobias Schwedes, Ben Calderhead Rate-Regularization and Generalization in Variational Autoencoders
Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana Brooks, Jennifer Dy, Jan-Willem van de Meent Regret Minimization for Causal Inference on Large Treatment Space
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima Regret-Optimal Filtering
Oron Sabag, Babak Hassibi Regularized ERM on Random Subspaces
Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco Regularized Policies Are Reward Robust
Hisham Husain, Kamil Ciosek, Ryota Tomioka Robust and Private Learning of Halfspaces
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen Robust Imitation Learning from Noisy Demonstrations
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama Robust Learning Under Strong Noise via SQs
Ioannis Anagnostides, Themis Gouleakis, Ali Marashian Sample Efficient Learning of Image-Based Diagnostic Classifiers via Probabilistic Labels
Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth, Jeevesh Kapur, Jacob Jaremko, Russell Greiner Sample Elicitation
Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang Scalable Gaussian Process Variational Autoencoders
Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch Semi-Supervised Learning with Meta-Gradient
Taihong Xiao, Xin-Yu Zhang, Haolin Jia, Ming-Ming Cheng, Ming-Hsuan Yang Shadow Manifold Hamiltonian Monte Carlo
Chris Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese Shuffled Model of Differential Privacy in Federated Learning
Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, Ananda Theertha Suresh Sketch Based Memory for Neural Networks
Rina Panigrahy, Xin Wang, Manzil Zaheer Spectral Tensor Train Parameterization of Deep Learning Layers
Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool Stable ResNet
Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau Stochastic Bandits with Linear Constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang Stochastic Linear Bandits Robust to Adversarial Attacks
Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett The Sample Complexity of Level Set Approximation
François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz The Teaching Dimension of Kernel Perceptron
Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen Top-M Identification for Linear Bandits
Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal Tractable Contextual Bandits Beyond Realizability
Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey Transforming Gaussian Processes with Normalizing Flows
Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Joern-Henrik Jacobsen Understanding the Wiring Evolution in Differentiable Neural Architecture Search
Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin