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.





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