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 I

27 July - 06:00-06:01 PT - Welcome

27 July - 06:01-06:50 PT - Keynote talk - Susan Murphy

27 July - 07:00-08:00 PT - Bayesian Methods

  • 8: The Neural Moving Average Model for Scalable Variational Inference of State Space Models - Tom Ryder ; Dennis Prangle ; Andrew Golightly ; Isaac Matthews
  • 227: Asynchronous ε-Greedy Bayesian Optimisation - George De Ath ; Richard Everson ; Jonathan Fieldsend
  • 389: Probabilistic Selection of Inducing Points in Sparse Gaussian Processes - Anders Kirk Uhrenholt ; Valentin Charvet ; Bjoern Sand Jensen
  • 601: A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery - Philip Boeken ; Joris M. Mooij

27 July - 08:10-09:10 PT - Graphical Models

  • 127: Approximate Implication with d-Separation - Batya Kenig
  • 480: Extendability of Causal Graphical Models: Algorithms and Computational Complexity - Marcel Wienöbst ; Max Bannach ; Maciej Liskiewicz
  • 668: Markov Equivalence of Max-Linear Bayesian Networks - Carlos Amendola ; Benjamin Hollering ; Seth Sullivant ; Ngoc Tran
  • 679: Partial Identifiability in Discrete Data With Measurement Error - Noam Finkelstein ; Roy Adams ; Suchi Saria ; Ilya Shpitser

27 July - 09:20-10:00 PT - Lightning I

  • 30: Variational Inference with Continuously-Indexed Normalizing Flows - Anthony L Caterini ; Rob Cornish ; Dino Sejdinovic ; Arnaud Doucet
  • 42: Competitive Policy Optimization - Manish Kumar Prajapat ; Kamyar Azizzadenesheli ; Alexander Liniger ; Yisong Yue ; Animashree Anandkumar
  • 46: Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization - Chunwei Ma ; Ziyun Huang ; Jiayi Xian ; Mingchen Gao ; Jinhui Xu
  • 66: The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization - Yifei Min ; Lin Chen ; Amin Karbasi
  • 72: XOR-SGD: Provable Convex Stochastic Optimization for Decision-making under Uncertainty - Fan Ding ; Yexiang Xue
  • 89: Path Dependent Structural Equation Models - Ranjani Srinivasan ; Jaron Jia Rong Lee ; Rohit Bhattacharya; Ilya Shpitser
  • 95: Application of Kernel Hypothesis Testing on Set-valued Data - Alexis Bellot ; Mihaela van der Schaar
  • 108: Constrained Labeling for Weakly Supervised Learning - Chidubem G Arachie ; Bert Huang
  • 112: Communication Efficient Parallel Reinforcement Learning - Mridul Agarwal ; Bhargav Ganguly ; Vaneet Aggarwal
  • 124: Matrix games with bandit feedback - Brendan O'Donoghue ; Tor Lattimore ; Ian Osband
  • 126: Improving Approximate Optimal Transport Distances using Quantization - Gaspard Beugnot ; Aude Genevay ; Justin M Solomon ; Kristjan Greenewald
  • 138: Lifted Reasoning Meets Weighted Model Integration - Leon Jonathan Feldstein ; Vaishak Belle
  • 144: Formal Verification of Neural Networks for Safety-Critical Tasks in Deep Reinforcement Learning - Davide Corsi ; Enrico Marchesini ; Alessandro Farinelli
  • 163: Action Redundancy in Reinforcement Learning - Nir Baram ; Tennenholtz Guy ; Shie Mannor
  • 167: Weighted Model Counting with Conditional Weights for Bayesian Networks - Paulius Dilkas ; Vaishak Belle
  • 171: Unsupervised Program Synthesis for Images By Sampling Without Replacement - Chenghui Zhou ; Chun-Liang Li ; Barnabas Poczos
  • 180: Bandits with Partially Observable Confounded Data - Tennenholtz Guy ; Uri Shalit ; Shie Mannor ; Yonathan Efroni
  • 192: A Weaker Faithfulness Assumption based on Triple Interactions - Alexander Marx ; Arthur Gretton ; Joris M. Mooij
  • 193: pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules - Michael Kirchhof ; Lena Schmid ; Christopher Reining ; Michael ten Hompel ; Markus Pauly
  • 195: Regstar: Efficient Strategy Synthesis for Adversarial Patrolling Games - David Klaska ; Antonin Kucera ; Vit Musil ; Vojtech Rehak
  • 231: Global Explanations with Decision Rules: a Co-learning Approach - Géraldin Nanfack ; Paul Temple ; Benoît Frénay
  • 233: A Unifying Framework for Observer-Aware Planning and its Complexity - Shuwa Miura ; Shlomo Zilberstein
  • 234: A Heuristic for Statistical Seriation - Komal Dhull ; Jingyan Wang ; Nihar Shah ; Yuanzhi Li ; R Ravi
  • 242: Exploring the Loss Landscape in Neural Architecture Search - Colin White ; Sam Nolen ; Yash Savani
  • 274: Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting - Adam D Cobb ; Brian Jalaian
  • 279: Decentralized Multi-Agent Active Search for Sparse Signals - Ramina Ghods ; Arundhati Banerjee ; Jeff Schneider
  • 313: Dependency in DAG models with Hidden Variables - Robin Evans
  • 339: Time-Variant Variational Transfer for Value Functions - Giuseppe Canonaco ; Andrea Soprani ; Matteo Giuliani ; Andrea Castelletti ; Manuel Roveri ; Marcello Restelli
  • 350: On Random Kernels of Residual Architectures - Etai Littwin; Tomer Galanti ; Lior Wolf
  • 351: Neural Markov Logic Networks - Giuseppe Marra ; Ondrej Kuzelka
  • 354: On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks - Martin Ferianc ; Partha Maji ; Matthew Mattina ; Miguel Rodrigues
  • 355: GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data - Jens Petersen ; Gregor Koehler ; David Zimmerer ; Fabian Isensee ; Paul Jäger ; Klaus H. Maier-Hein

27 July - 10:00-11:30 PT - Posters I

Posters of papers from long and lightning talks of Bayesian Methods, Graphical Models, Lightning I.





Sponsors