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 Ef´Čücient 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.





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