UAI 2019 – VIDEOS
All videos are available to watch both here and on the UAI 2019 YouTube Channel.
(Including invited speakers, students lectures, tutorials & presentations)
The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).
Tutorial 1: Tractable Probabilistic Models: Representations, Algorithms, Learning, and Applications.
Guy Van den Broeck (UCLA), Nicola Di Mauro (Università degli Studi di Bari “Aldo Moro” ), Antonio Vergari (UCLA)
Tutorial 2: Mixing Graphical Models and Neural Nets Like Chocolate and Peanut Butter.
Matt Johnson (Google Brain)
Tutorial 3: Causal Reinforcement Learning.
Elias Bareinboim (Columbia University)
Tutorial 4: Mathematics of Deep Learning.
Raja Giryes (Tel Aviv University)
Invited Speaker (Rina Dechter, UC Irvine): Anytime Probabilistic Reasoning.
Lecture (ID 264): Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation.
Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
Lecture (ID 356): Generating and Sampling Orbits for Lifted Probabilistic Inference.
Steven Holtzen, Todd Millstein, Guy Van den Broeck
Lecture (ID 6): Conditional Expectation Propagation.
Zheng Wang, Shandian Zhe
Lecture (ID 221): Belief Propagation: Accurate Marginals or Accurate Partition Function – Where is the Difference?
Christian Knoll, Franz Pernkopf
Lecture (ID 204): Sliced Score Matching: A Scalable Approach to Density and Score Estimation.
Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
Lecture (ID 124): Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting, Zoubin Ghahramani
Lecture (ID 91): Towards a Better Understanding and Regularization of GAN Training Dynamics
Weili Nie, Ankit Patel
Lecture (ID 159): Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Qi She, Anqi Wu
Lecture (ID 164): Efficient Neural Network Verification with Exactness Characterization.
Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli
Lecture (ID 253): Sinkhorn AutoEncoders
Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
Lecture (ID 244): Learning with Non-Convex Truncated Losses by SGD.
Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
Invited Speaker (Suchi Saria, Johns Hopkins University): Safety Challenges with Black-Box Predictors and Novel Learning Approaches for Failure Proofing.
Invited Speaker (Stefanie Jegelka, MIT): Safety Challenges with Black-Box Predictors and Novel Learning Approaches for Failure Proofing.
Lecture (ID 144): General Identifiability with Arbitrary Surrogate Experiments.
Sanghack Lee, Juan D. Correa, Elias Bareinboim
Lecture (ID 481): On Open-Universe Causal Reasoning.
Duligur Ibeling, Thomas Icard
Lecture (ID 210): Approximate Causal Abstractions.
Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
Lecture (ID 205): Beyond Structural Causal Models: Causal Constraints Models.
Tineke Blom, Stephan Bongers, Joris M. Mooij
Lecture (ID 512): Exclusivity Graph Approach to Instrumental Inequalities.
Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino
Lecture (ID 222): Finding Minimal d-separators in Linear Time and Applications.
Benito van der Zander, Maciej Liśkiewicz
Invited Speaker (Emma Brunskill, Stanford University, CS Cornell): Towards Efficient Effective Reinforcement Learning Algorithms That Interact With People.
Lecture (ID 440): Off-Policy Policy Gradient with Stationary Distribution Correction.
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Lecture (ID 315): Wasserstein Fair Classification.
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Lecture (ID 158): A Fast Proximal Point Method for Computing Exact Wasserstein Distance.
Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha
Invited Speaker (Yee Whey Teh, University of Oxford & Deepmind): A Probabilistic Perspective on Meta and Reinforcement Learning. (Slides)
Lecture (ID 345): Exact Sampling of Directed Acyclic Graphs from Modular Distributions.
Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto
Lecture (ID 468): Bayesian Optimization with Binary Auxiliary Information.
Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
Lecture (ID 176): Perturbed-History Exploration in Stochastic Linear Bandits.
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier
Lecture (ID 248): Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank.
Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Zheng Wen, Branislav Kveton
Lecture (ID 267): A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations.
Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
Lecture (ID 407): On the Relationship Between Satisfiability and Markov Decision Processes.
Ricardo Salmon, Pascal Poupart
Lecture (ID 228): A Bayesian Approach to Robust Reinforcement Learning.
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Lecture (ID 191): An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient.
Pan Xu, Felicia Gao, Quanquan Gu
Lecture (ID 441): Co-training for Policy Learning.
Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono
Lecture (ID 21): Truly Proximal Policy Optimization.
Yuhui Wang, Hao He, Xiaoyang Tan
Lecture (ID 14): Correlated Learning for Aggregation Systems.
Tanvi Verma, Pradeep Varakantham
Lecture (ID 49): Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory.
Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schoelkopf
Lecture (ID 335): Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning.
Smitha Milli, Anca D. Dragan
Lecture (ID 64): Randomized Iterative Algorithms for Fisher Discriminant Analysis.
Agniva Chowdhury, Jiasen Yang, Petros Drineas
Lecture (ID 28): Countdown Regression: Sharp and Calibrated Survival Predictions
Anand Avati, Tony Duan, Sharon Zhou, Ken Jung, Nigam H. Shah, Andrew Ng
UAI 2019 will be held in Tel Aviv, Israel in July 2019.
The Association for Uncertainty in Artificial Intelligence is a non-profit organization focused on organizing the annual Conference on Uncertainty in Artificial Intelligence (UAI) and, more generally, on promoting research in pursuit of advances in knowledge representation, learning and reasoning under uncertainty. The most recent UAI conference was the 34th conference, UAI-2018 in Monterey, California, USA. Join our Facebook page or add yourself to the UAI Mailing list to keep updated on announcements and relevant AI news.
Principles and applications developed within the UAI community have been at the forefront of research in Artificial Intelligence. The UAI community and annual meeting have been primary sources of advances in graphical models for representing and reasoning with uncertainty.
The UAI conference has been held every year since 1985. Hardcopy versions of the proceedings can be purchased through Brightdoc.
Most UAI conference papers are available in electronic form at the online UAI proceedings archive.
- Collections of papers from the first six UAI conferences, 1985-1990, were published as edited books by North-Holland under the title Uncertainty in Artificial Intelligence (1-6).
- Between 1991 and 2003, proceedings were published by Morgan Kaufmann Publishers and were distributed at the conference by Morgan Kaufmann.
- Since 2004, the UAI proceedings have been published by the AUAI Press, the Association for Uncertainty in Artificial Intelligence’s own press.
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