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UAI 2021 Program

For the final published version of the papers, please use the Proceedings of Machine Learning Research - Volume 161. Links below may point to older versions.

Access the blocks of the conference using the following links:

Block V

29 July - 06:00-06:01 PT - Welcome

29 July - 06:01-06:50 PT - Keynote talk - Ankur Moitra

29 July - 07:00-08:00 PT - Estimation / Testing

  • 26: Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling - Kei Ishikawa ; Takashi Goda
  • 96: A Kernel Two-Sample Test with Selection Bias - Alexis Bellot ; Mihaela van der Schaar
  • 281: Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains - Francisco Ruiz ; Michalis Titsias ; Taylan Cemgil ; Arnaud Doucet
  • 471: Testification of Condorcet Winners in Dueling Bandits - Björn Haddenhorst ; Viktor Bengs ; Jasmin Brandt ; Eyke Hüllermeier

29 July - 08:10-09:10 PT - Robustness

  • 275: Robust Principal Component Analysis for Generalized Multi-View Models - Frank Nussbaum ; Joachim Giesen
  • 282: Possibilistic Preference Elicitation by Minimax Regret - Loic Adam ; Sebastien Destercke
  • 321: Distribution-free uncertainty quantification for classification under label shift - Aleksandr Podkopaev ; Aaditya Ramdas
  • 566: Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains - Jasper De Bock ; Alexander Erreygers ; Thomas Krak

29 July - 09:20-10:00 PT - Lightning V

  • 591: Contingency-Aware Influence Maximization: A Reinforcement Learning Approach - Haipeng Chen ; Wei Qiu ; Han-Ching Ou ; Bo An ; Milind Tambe
  • 600: Generating Adversarial Examples with Graph Neural Networks - Florian Jaeckle ; M. Pawan Kumar
  • 606: Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning - Robert Loftin ; Aadirupa Saha ; Sam Devlin ; Katja Hofmann
  • 611: Combining Pseudo-Point and State Space Approximationsfor Sum-Separable Gaussian Processes - William Tebbutt ; Arno Solin ; Richard E. Turner
  • 634: An Optimization and Generalization Analysis for Max-Pooling Networks - Alon Brutzkus ; Amir Globerson
  • 635: Investigating Vulnerabilities of Deep Neural Policies - Ezgi Korkmaz
  • 649: Random Probabilistic Circuits - Nicola Di Mauro ; Gennaro Gala ; Marco Iannotta ; Teresa M. A. Basile
  • 650: Multi-Task and Meta-Learning with Sparse Linear Bandits - Leonardo Cella ; Massimiliano Pontil
  • 652: Federated Stochastic Gradient Langevin Dynamics - Khaoula El mekkaoui ; Diego Mesquita ; Paul Blomstedt ; Samuel Kaski
  • 653: Certification of Iterative Predictions in Bayesian Neural Networks - Matthew R Wicker ; Luca Laurenti ; Andrea Patane ; Nicola Paoletti ; Alessandro Abate ; Marta Kwiatkowska
  • 662: Integer Programming-based Error-Correcting Output Code Design for Robust Classification - Samarth Gupta ; Saurabh Amin
  • 663: Statistically Robust Neural Network Classification - Benjie Wang ; Stefan Webb ; Tom Rainforth
  • 669: Constrained Differentially Private Federated Learning for Low-bandwidth Devices - Raouf Kerkouche ; Gergely Acs ; Claude A Castelluccia ; Pierre Genevès
  • 672: Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection - Dennis Ulmer ; Giovanni Cinà
  • 676: Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts - Mike Gimelfarb ; Scott Sanner ; Chi-Guhn Lee
  • 681: Bias-Corrected Peaks-Over-Threshold Estimation of the CVaR - Dylan Troop ; Frederic Godin ; Jia Yuan Yu
  • 686: Non-PSD Matrix Sketching with Applications to Regression and Optimization - Zhili Feng ; Fred Roosta ; David Woodruff
  • 694: Convergence Behavior of Belief Propagation: Estimating Regions of Attraction via Lyapunov Functions - Harald Leisenberger ; Christian Knoll ; Richard Seeber ; Franz Pernkopf
  • 718: Active multi-fidelity Bayesian online changepoint detection - Gregory Gundersen ; Diana Cai ; Chuteng Zhou ; Barbara Engelhardt ; Ryan P Adams
  • 719: Learning in Multi-Player Stochastic Games - William Brown
  • 735: CORe: Capitalizing On Rewards in Bandit Exploration - Nan Wang ; Branislav Kveton ; Maryam Karimzadehgan
  • 745: Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification - Yu Wang ; Yuesong Shen ; Daniel Cremers
  • 750: Statistical Mechanical Analysis of Neural Network Pruning - Rupam Acharyya ; Boyu Zhang ; Ankani Chattoraj ; Shouman Das ; Daniel Stefankovic
  • 752: Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks - Adrià Garriga-Alonso ; Mark van der Wilk
  • 753: Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression - Zhongjie Yu ; Mingye Zhu ; Martin Trapp ; Arseny Skryagin ; Kristian Kersting
  • 761: Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation - Amirhossein Kardoost ; Margret Keuper
  • 767: Learning Probabilistic Sentential Decision Diagrams Under Logic Constraints by Sampling and Averaging - Renato L Geh ; Denis D Maua
  • 771: Efficient Online Inference for Nonparametric Mixture Models - Rylan Schaeffer ; Blake A Bordelon ; Mikail Khona ; Weiwei Pan ; Ila Fiete
  • 798: Towards a Unified Framework for Fair and Stable Graph Representation Learning - Chirag Agarwal ; Himabindu Lakkaraju ; Marinka Zitnik
  • 814: Identifying Regions of Trusted Predictions - Nivasini Ananthakrishnan ; Shai Ben-David ; Tosca Lechner ; Ruth Urner
  • 817: Learning and Certification under Instance-targeted Poisoning - Ji Gao ; Amin Karbasi ; Mohammad Mahmoody

29 July - 10:00-11:30 PT - Posters V

Posters of papers from long and lightning talks of Estimation / Testing, Robustness, Lightning V.





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