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 II

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

27 July - 18:01-18:50 PT - Keynote talk - Eric Horvitz

27 July - 19:00-20:00 PT - Causality

  • 57: Causal Additive Models with Unobserved Variables - Takashi Nicholas Maeda ; Shohei Shimizu
  • 549: Causal Markov Boundaries - Sofia Triantafillou ; Fattaneh Jabbari ; Gregory F Cooper
  • 593: Invariant Representation Learning for Treatment Effect Estimation - Claudia Shi ; Victor Veitch ; David Blei
  • 781: Disentangling Mixtures of Unknown Causal Interventions - Abhinav Kumar ; Gaurav Sinha

27 July - 20:10-21:10 PT - Density Estimation

  • 90: Featurized Density Ratio Estimation - Kristy Choi ; Madeline Liao ; Stefano Ermon
  • 241: Generative Archimedean Copulas - Yuting Ng ; Ali Hasan ; Khalil Elkhalil ; Vahid Tarokh
  • 683: Diagnostics for Conditional Density Models and Bayesian Inference Algorithms - David Zhao ; Niccolo Dalmasso ; Rafael Izbicki ; Ann B. Lee
  • 822: Min/Max Stability and Box Distributions - Michael J Boratko ; Javier Burroni ; Shib Sankar Dasgupta ; Andrew McCallum

27 July - 21:20-22:00 PT - Lightning II

  • 16: Task Similarity Aware Meta Learning: Theory-inspired Improvement on MAML - Pan Zhou ; Yingtian Zou ; Xiao-Tong Yuan ; Jiashi Feng ; Caiming Xiong ; Steven Hoi
  • 32: TreeBERT: A Tree-Based Pre-Trained Model for Programming Language - Xue Jiang ; Zhuoran Zheng ; Chen Lyu ; Liang Li ; Lei Lyu
  • 51: Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation - Takeshi Teshima ; Masashi Sugiyama
  • 58: A Variational Approximation for Analyzing the Dynamics of Panel data - Jurijs Nazarovs ; Rudrasis Chakraborty ; Songwong Tasneeyapant ; Sathya Ravi ; Vikas Singh
  • 59: Graph Reparametrizations for Enabling 1000+ Monte Carlo Iterations In Bayesian Deep Neural Networks - Jurijs Nazarovs ; Ronak Mehta ; Vishnu Suresh Lokhande ; Vikas Singh
  • 68: Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning - Yizhao Gao ; Nanyi Fei ; Guangzhen Liu ; Zhiwu Lu ; Tao Xiang
  • 92: Variance Reduction in Frequency Estimators via Control Variates Method - Rameshwar Pratap ; Raghav Kulkarni
  • 103: An Unsupervised Video Game Playstyle Metric via State Discretization - Chiu-Chou Lin ; Wei-Chen Chiu ; I-Chen Wu
  • 106: MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning - Tuan Nguyen ; Trung Le ; He Zhao ; Quan Hung Tran ; Truyen V. Nguyen ; Dinh Phung
  • 120: Defending SVMs Against Poisoning Attacks: The Hardness and DBSCAN Approach - Hu Ding ; Fan Yang ; Jiawei Huang
  • 133: Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation - Simon Luo ; Lamiae Azizi ; Mahito Sugiyama
  • 146: Symmetric Wasserstein Autoencoders - Sun Sun ; Hongyu Guo
  • 177: On the Distributional Properties of Adaptive Gradients - ZhiYi Zhang ; Liu Ziyin
  • 184: Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle - Xin-Yu Zhang ; KAI ZHAO ; Taihong Xiao ; Ming-Ming Cheng ; Ming-Hsuan Yang
  • 205: The Complexity of Nonconvex-Strongly-Concave Minimax Optimization - Siqi Zhang ; Junchi Yang ; Cristobal Guzman ; Negar Kiyavash ; Niao He
  • 207: High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces - David Eriksson ; Martin Jankowiak
  • 212: Dynamic visualization for L1 fusion convex clustering in near-linear time - Bingyuan Zhang ; Jie Chen ; Yoshikazu Terada
  • 214: FlexAE: Flexibly Learning Latent Priors for Wasserstein Auto-Encoders - Arnab K Mondal ; Himanshu Asnani ; Parag Singla ; Prathosh A P
  • 219: Efficient Greedy Coordinate Descent via Variable Partitioning - Huang Fang ; Guanhua Fang ; Tan Yu ; Ping Li
  • 220: Bayesian Streaming Sparse Tucker Decomposition - Shikai Fang ; Robert Kirby ; Shandian Zhe
  • 222: A Nonmyopic Approach to Cost-Constrained Bayesian Optimization - Eric H Lee ; David Eriksson ; Valerio Perrone ; Matthias Seeger

27 July - 22:00-23:30 PT - Posters II

Posters of papers from long and lightning talks of Causality, Density Estimation, Lightning II.





Sponsors