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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 IV

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

28 July - 18:01-18:50 PT - Keynote talk - Judea Pearl

28 July - 19:00-20:00 PT - Dimensionality Reduction

  • 405: Hierarchical Infinite Relational Model - Feras Saad ; Vikash Mansinghka
  • 713: Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data - Aaron Schein ; Anjali Nagulpally ; Hanna Wallach ; Patrick Flaherty
  • 770: Exact and Approximate Hierarchical Clustering Using A* - Craig S Greenberg ; Sebastian Macaluso ; Nicholas Monath ; Kumar Avinava Dubey ; Patrick Flaherty ; Manzil Zaheer ; Amr Ahmed ; Kyle Cranmer ; Andrew McCallum
  • 841: Sequential Core-Set Monte Carlo - Boyan Beronov ; Christian Weilbach ; Frank Wood ; Trevor Campbell

28 July - 20:10-21:10 PT - Privacy / Theory

  • 218: Generalized Parametric Path Problems - Kshitij Gajjar ; Girish Varma ; Prerona Chatterjee ; Jaikumar Radhakrishnan
  • 289: Measuring Data Leakage in Machine-Learning Models with Fisher Information - Awni Hannun ; Chuan Guo ; Laurens van der Maaten
  • 441: Principal Component Analysis in the Stochastic Differential Privacy Model - Fanhua Shang ; Zhihui Zhang ; Tao Xu ; Yuanyuan Liu ; Hongying Liu
  • 829: Geometric Rates of Convergence for Kernel-based Sampling Algorithms - Rajiv Khanna ; Liam Hodgkinson ; Michael Mahoney

28 July - 21:20-22:00 PT - Lightning IV

  • 238: LocalNewton: Reducing Communication Bottleneck for Distributed Learning - Vipul Gupta ; Avishek Ghosh ; Michal Derezinski ; Rajiv Khanna ; Ramchandran Kannan ; Michael Mahoney
  • 262: Finite-Time Theory for Momentum Q-learning - Bowen Weng ; Huaqing Xiong ; Lin Zhao ; Yingbin Liang ; Wei Zhang
  • 284: When is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems? - Simon Du ; Wei Hu ; Zhiyuan Li ; Ruoqi Shen ; Zhao Song ; Jiajun Wu
  • 286: Thompson Sampling for Markov Games with Piecewise Stationary Opponent Policies - Anthony DiGiovanni ; Ambuj Tewari
  • 299: Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces - Akiyoshi Sannai ; Masaaki Imaizumi ; Makoto Kawano
  • 304: Probabilistic task modelling for meta-learning - Cuong Cao Nguyen ; Thanh-Toan Do ; Gustavo Carneiro
  • 305: Approximation Algorithm for Submodular Maximization under Submodular Cover - Naoto Ohsaka ; Tatsuya Matsuoka
  • 308: Tighter Generalization Bounds for Iterative Privacy-Preserving Algorithms - Fengxiang He ; Bohan Wang ; Dacheng Tao
  • 315: Natural Language Adversarial Defense through Synonym Encoding - Xiaosen Wang ; Hao Jin ; Yichen Yang ; Kun He
  • 319: Path-BN: Towards Effective Batch Normalization in the Path Space for ReLU Networks - Xufang Luo ; Qi Meng ; Wei Chen ; Yunhong Wang ; Tie-Yan Liu
  • 330: Combinatorial Semi-Bandit in the Non-Stationary Environment - Wei Chen ; Liwei Wang ; Haoyu Zhao ; Kai Zheng
  • 370: No-Regret Learning with High-Probability in Adversarial Markov Decision Processes - Mahsa Ghasemi ; AbolfazL Hashemi ; Haris Vikalo ; Ufuk Topcu
  • 434: Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network - Sambaran Bandyopadhyay ; Vishal Peter
  • 445: Variance-Dependent Best Arm Identification - Pinyan Lu ; Chao Tao ; Xiaojin Zhang
  • 452: Stochastic Continuous Normalizing Flows: Training SDEs as ODEs - Liam Hodgkinson ; Chris van der Heide ; Fred Roosta ; Michael Mahoney
  • 456: On The Distribution of Penultimate Activations of Classification Networks - Minkyo Seo ; Yoonho Lee ; Suha Kwak
  • 462: Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling - Difan Zou ; Pan Xu ; Quanquan Gu
  • 466: Tractable Computation of Expected Kernels - Wenzhe Li ; Zhe Zeng ; Antonio Vergari ; Guy Van den Broeck
  • 491: Trumpets: Injective Flows for Inference and Inverse Problems - Konik Kothari ; AmirEhsan Khorashadizadeh ; Maarten de Hoop ; Ivan Dokmanic
  • 519: ReZero is All You Need: Fast Convergence at Large Depth - Thomas Bachlechner ; Bodhisattwa Prasad Majumder ; Huanru Henry H Mao ; Garrison Cottrell ; Julian McAuley
  • 527: Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression - Ayush Jain ; P. K. Srijith ; Mohammad Emtiyaz Khan

28 July - 22:00-23:30 PT - Posters IV

Posters of papers from long and lightning talks of Dimensionality Reduction, Privacy / Theory, Lightning IV.





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