Conference Important Dates Code of Conduct Discussants Workshops Registration Proceedings Sponsors Program
Organization Organizing Committee Senior Program Committee Program Committee Reviewing Instructions AUAI
For Authors Call for Papers Call for Workshops Submission Instructions Conflicts of Interest Subject Areas Camera-ready Instructions

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 VI

29 July - 18:00-18:01 PT - Welcome

29 July - 18:01-18:50 PT - Townhall

29 July - 19:00-20:00 PT - Reinforcement Learning

  • 116: Robust Reinforcement Learning Under Minimax Regret for Green Security - Lily Xu ; Andrew Perrault ; Fei Fang ; Haipeng Chen ; Milind Tambe
  • 169: Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization - Jyun-Li Lin ; Wei Hung ; Shang Hsuan Yang ; Ping-Chun Hsieh ; Xi Liu
  • 210: Known unknowns: Learning novel concepts using reasoning-by-elimination - Harsh Agrawal ; eli meirom ; Yuval Atzmon ; Shie Mannor ; Gal Chechik
  • 558: CLAIM: Curriculum Learning Policy for Influence Maximization in UnknownSocial Networks - Dexun Li ; Meghna Lowalekar ; Pradeep Varakantham

29 July - 20:10-21:10 PT - Deep Models

  • 154: Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders - Gowthami Somepalli ; Yexin Wu ; Yogesh Balaji ; Bhanukiran Vinzamuri ; Soheil Feizi
  • 353: Deep Kernels with Probabilistic Embeddings for Small-Data Learning - Ankur Mallick ; Chaitanya Dwivedi ; Bhavya Kailkhura ; Gauri Joshi ; T. Yong-Jin Han
  • 590: RISAN: Robust Instance Specific Deep Abstention Network - Bhavya Kalra ; Kulin Shah ; Naresh Manwani
  • 732: Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks - Apoorva Sharma ; Navid Azizan ; Marco Pavone

29 July - 21:20-22:00 PT - Lightning VI

  • 554: Simple Combinatorial Algorithms for Combinatorial Bandits: Corruptions and Approximations - Haike Xu ; Jian Li
  • 560: Learning to Learn with Gaussian Processes - Quoc Phong Nguyen ; Bryan Kian Hsiang Low ; Patrick Jaillet
  • 568: Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization - Quoc Phong Nguyen ; Zhaoxuan Wu ; Bryan Kian Hsiang Low ; Patrick Jaillet
  • 570: Minimax Sample Complexity for Turn-based Stochastic Game - Qiwen Cui ; Lin Yang
  • 603: Graph-Based Semi-Supervised Learning through the Lens of Safety - Shreyas S ; Avirup Saha ; Priyank M Patel ; Samik Datta ; Niloy Ganguly
  • 613: Class Balancing GAN with a Classifier in the Loop - Harsh Rangwani ; Konda Reddy Mopuri ; Venkatesh Babu RADHAKRISHNAN
  • 619: Hierarchical Learning of Hidden Markov Models with Clustering Regularization - Hui Lan ; Antoni Chan
  • 624: Enabling long-range exploration in minimization of multimodal functions - Jiaxin Zhang ; Hoang Tran ; Dan Lu ; Guannan Zhang
  • 638: Modeling Financial Uncertainty with Multivariate Temporal Entropy-based Curriculums - Ramit Sawhney ; Arnav Wadhwa ; Ayush Mangal ; Vivek Mittal ; Shivam Agarwal ; Rajiv Ratn Shah
  • 673: Nearest Neighbor Search Under Uncertainty - Blake Mason ; Ardhendu S Tripathy ; Robert Nowak
  • 682: Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence - Ghassen Jerfel ; Serena L Wang ; Clara Fannjiang ; Katherine Heller ; Yian Ma ; Michael Jordan
  • 690: Staying in Shape: Learning Invariant Shape Representations using ContrastiveLearning - Jeffrey Gu ; Serena Yeung
  • 695: Explaining Fast Improvement in Online Imitation Learning - Xinyan Yan ; Byron Boots ; Ching-An Cheng
  • 701: Gradient-based Optimization for Multi-resource Spatial Coverage Problems - Nitin Kamra ; Yan Liu
  • 716: SGD with Low-Dimensional Gradients with Applications to Private and Distributed Learning - Shiva Kasiviswanathan
  • 722: q-Paths: Generalizing the Geometric Annealing Path using Power Means - Vaden W Masrani ; Rob Brekelmans ; Thang D Bui ; Frank Nielsen ; Aram Galstyan ; Greg Ver Steeg ; Frank Wood
  • 725: Condition Number Bounds for Causal Inference - Spencer Gordon ; Vinayak M Kumar ; Leonard J Schulman ; Piyush Srivastava
  • 758: PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition - Polina Zablotskaia ; Edoardo Alberto Dominici ; Leonid Sigal ; Andreas Lehrmann
  • 768: Conditionally Independent Data Generation - Kartik Ahuja ; Prasanna Sattigeri ; Karthikeyan Shanmugam ; Dennis Wei ; Karthikeyan Natesan Ramamurthy ; Murat Kocaoglu
  • 776: No-Regret Approximate Inference via Bayesian Optimisation - Rafael Oliveira ; Lionel Ott ; Fabio Ramos
  • 793: SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning - Shabnam Daghaghi ; Tharun Medini ; Anshumali Shrivastava

29 July - 22:00-23:30 PT - Posters VI

Posters of papers from long and lightning talks of Reinforcement Learning, Deep Models, Lightning VI.





Sponsors