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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 III

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

28 July - 06:01-06:50 PT - Keynote talk - Lenka Zdeborová

28 July - 07:00-08:00 PT - Neural Networks

  • 145: Learnable Uncertainty under Laplace Approximations - Agustinus Kristiadi ; Matthias Hein ; Philipp Hennig
  • 287: Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning - Samuel Kessler ; Vu Nguyen ; Stefan Zohren ; Stephen Roberts
  • 328: Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis - Leo Schwinn ; An Nguyen ; René Raab ; Leon Bungert ; Daniel Tenbrinck ; Dario Zanca ; Martin Burger ; Bjoern Eskofier
  • 607: Information Theoretic Meta Learning with Gaussian Processes - Michalis Titsias ; Francisco Ruiz ; Sotirios Nikoloutsopoulos ; Alexandre Galashov

28 July - 08:10-09:10 PT - Explainability / Fairness

  • 232: Addressing Fairness in Classification with a Model-Agnostic Multi-ObjectiveAlgorithm - Kirtan S Padh ; Diego Antognini ; Emma LEJAL GLAUDE ; Boi Faltings ; Claudiu Musat
  • 342: BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations - Xingyu Zhao ; Wei Huang ; Xiaowei Huang ; Valentin Robu ; David Flynn
  • 478: Classification with abstention but without disparities - Nicolas Schreuder ; Evgenii Chzhen
  • 531: Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice - David Watson ; Limor Gultchin ; Ankur Taly ; Luciano Floridi

28 July - 09:20-10:00 PT - Lightning III

  • 359: Mixed Variable Bayesian Optimization with Frequency Modulated Kernels - Changyong Oh ; Efstratios Gavves ; Max Welling
  • 361: Subseasonal Climate Prediction in the Western US using Bayesian Spatial Models - Vishwak Srinivasan ; Justin Khim ; Arindam Banerjee ; Pradeep Ravikumar
  • 367: Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference - Antonio K Moretti ; Liyi Zhang ; Christian A Naesseth ; Hadiah Venner ; David Blei ; Itsik Pe'er
  • 369: Estimating Treatment Effects with Observed Confounders and Mediators - Shantanu Gupta ; Zachary Lipton ; David Childers
  • 378: A Decentralized Policy Gradient Approach to Multi-Task Reinforcement Learning - Sihan Zeng ; Aqeel Anwar ; Thinh T Doan ; Arijit Raychowdhury ; Justin Romberg
  • 379: Compositional Abstraction Error and a Category of Causal Models - Eigil F Rischel ; Sebastian Weichwald
  • 383: Bayesian optimization for modular black-box systems with switching costs - Chi-Heng Lin ; Joseph D Miano ; Eva Dyer
  • 392: Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables - Noam Finkelstein ; Beata Zjawin ; Elie Wolfe ; Ilya Shpitser ; Robert Spekkens
  • 403: Learning Proposals for Probabilistic Programs with Inference Combinators - Sam Stites ; Heiko Zimmermann ; Hao Wu ; Eli Z Sennesh ; Jan-Willem van de Meent
  • 435: NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation - Xiaohui Zeng ; Raquel Urtasun ; Richard Zemel ; Sanja Fidler ; Renjie Liao
  • 439: PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment - Fan Ding ; Nan Jiang ; Jianzhu Ma ; Jian Peng ; Jinbo Xu ; Yexiang Xue
  • 467: Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice - Nir Ailon; Omer Leibovitch ; Vineet Nair
  • 470: Uncertainty-aware sensitivity analysis using Rényi divergences - Topi Paananen ; Michael Andersen ; Aki Vehtari
  • 472: The Promises and Pitfalls of Deep Kernel Learning - Sebastian W Ober ; Carl Edward Rasmussen ; Mark van der Wilk
  • 473: Confidence in Causal Discovery with Linear Causal Models - David Strieder ; Tobias Freidling ; Stefan Haffner ; Mathias Drton
  • 479: Maximal Ancestral Graph Structure Learning via Exact Search - Kari M Rantanen ; Antti Hyttinen ; Matti Järvisalo
  • 489: Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting - Iwona Hawryluk ; Henrique Hoeltgebaum ; Swapnil Mishra ; Xenia Miscouridou ; Ricardo P Schnekenberg ; Charles Whittaker ; Michaela Vollmer ; Seth Flaxman ; Samir Bhatt ; Thomas A Mellan
  • 492: Stochastic Model for Sunk Cost Bias - Jon Kleinberg ; Sigal Oren ; Manish Raghavan ; Nadav Sklar
  • 500: Optimized Auxiliary Particle Filters - Nicola Branchini ; Victor Elvira
  • 502: Inference of Causal Effects when Control Variables are Unknown - Ludvig Hult ; Dave Zachariah
  • 505: Dimension reduction for data with heterogeneous missingness - Yurong Ling ; Zijing Liu ; Jing-Hao Xue
  • 512: Tensor-Train Density Estimation - Georgii Novikov ; Maxim Panov ; Ivan Oseledets
  • 515: Similarity Measure for Sparse Time Course Data Based on Gaussian Processes - Zijing Liu ; Mauricio Barahona
  • 516: Towards Robust Episodic Meta-Learning - Beyza Ermis ; Giovanni Zappella ; Cedric Archambeau
  • 528: PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components - Andrew Song ; Demba Ba ; Emery Brown
  • 535: Faster Lifting for Two-Variable Logic Using Cell Graphs - Timothy van Bremen ; Ondrej Kuzelka
  • 536: Post-hoc loss-calibration for Bayesian neural networks - Meet P Vadera ; Soumya Ghosh ; Kenney Ng ; Benjamin Marlin
  • 539: Towards Tractable Optimism in Model-Based Reinforcement Learning - Aldo Pacchiano ; Philip J Ball ; Jack Parker-Holder ; Krzysztof Choromanski ; Stephen Roberts
  • 540: Probabilistic DAG Search - Julia Grosse ; Cheng Zhang ; Philipp Hennig
  • 573: Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems - Yinchong Yang ; Florian Buettner
  • 582: Generalization Error Bounds for Deep Unfolding RNNs - Boris Joukovsky ; Tanmoy Mukherjee ; Huynh Van Luong ; Nikos Deligiannis

28 July - 10:00-11:30 PT - Posters III

Posters of papers from long and lightning talks of Neural Networks, Explainability / Fairness, Lightning III.





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