UAI 2023 - Schedule
Please note that all dates and times are EST (Eastern Standard Time). The conference is hybrid; most presentations, as well as poster sessions on Tuesday and Thursday will be in-person, however some presenters will be virtual. In addition, there will be a virtual poster session on Wednesday. All sessions of the main conference take place in McConomy Auditorium, Jared L. Cohon University Center, Carnegie Mellon University, Pittsburgh, USA.
A downloadable PDF version of the program is available here.
Zoom links:
For all sessions in McConomy Auditorium: https://tinyurl.com/UAI23Main;
for Connan Room: https://tinyurl.com/UAI23Connan;
for McKenna, Peter, and Wright Room: https://tinyurl.com/UAI23MPW;
for Studio Theater: https://tinyurl.com/UAI23Studio;
for the virtual poster session: https://tinyurl.com/UAI23VirtualPosters.
For a summary of the program, please see the table below:
| Time | 31-Jul (Mon) Tutorials |
1-Aug (Tue) Main conference day 1 |
2-Aug (Wed) Main conference day 2 |
3-Aug (Thurs) Main conference day 3 |
4-Aug (Fri) Workshops |
|---|---|---|---|---|---|
| 9:00 | Tutorials Connan Room, McKenna, Peter, and Wright Room, McConomy Auditorium |
Keynote: Alexandra Chouldechova McConomy Auditorium |
Oral Session 3 Causal inference and missing data McConomy Auditorium |
Keynote: Caroline Uhler McConomy Auditorium |
Workshops Studio Theater, McKenna, Peter, and Wright Room, Connan Room, McConomy Auditorium Lunch (boxed lunch) (morning coffee break in Rangos 3; afternoon coffee break informal) |
| 10:00 | Coffee break (30 minutes) Connan Room |
Coffee break (30 minutes) Connan Room |
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| 10:30 | Oral Session 1 Neural networks and Deep Learning McConomy Auditorium |
Oral Session 5 Methods for intractable learning problems McConomy Auditorium |
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| 10:40 | Coffee break (20 minutes) |
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| 11:00 | Virtual Poster Session https://tinyurl.com/UAI23VirtualPosters |
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| 11:30 | |||||
| 12:10 | Spotlight Session 1 McConomy Auditorium |
Spotlight Session 3 McConomy Auditorium |
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| 12:30 | Lunch (boxed lunch) | Lunch (boxed lunch) |
Lunch (boxed lunch) | ||
| 14:00 | Oral Session 2 Uncertainty quantification and calibration McConomy Auditorium |
Oral Session 4 Modelling and learning McConomy Auditorium |
Oral Session 6 Probabilistic circuit models McConomy Auditorium |
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| 15:00 | |||||
| 15:30 | |||||
| 15:40 | Spotlight Session 2 McConomy Auditorium |
Coffee break (20 minutes) Connan Room |
Spotlight Session 4 McConomy Auditorium |
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| 16:00 | Coffee break (30 minutes) Connan Room |
Keynote: Victor Chernozhukov McConomy Auditorium |
Coffee break (30 minutes) Connan Room |
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| 16:30 | In-person Poster Session 1 Rangos Hall |
In-person Poster Session 2 Rangos Hall |
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| 17:00 | Town Hall and Best Paper Awards McConomy Auditorium |
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| 18:15 | |||||
| 18:30 | Opening Reception Simmons A/B in Tepper Quad |
Break/walk to banquet | |||
| 19:00 | Banquet Phipps Conservatory and Botanical Gardens |
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| 19:30 | |||||
| 20:00 | |||||
| 21:00 |
| Time | Event |
|---|---|
| 9:00 | Connan Room: Towards Causal Foundations of Safe AI. James Fox, Tom Everitt [video] |
| 9:00 | McKenna, Peter, and Wright Room: Causal Representation Learning. Dhanya Sridhar, Jason Hartford [video] |
| 10:15 | Coffee Break (Rangos Hall) |
| 10:30 | Continuation of tutorials |
| 12:00 | Lunch (on your own) |
| 13:30 | McConomy Auditorium: Structure Learning Using Benchpress. Felix L. Rios, Giusi Moffa, Jack Kuipers [video] |
| 15:30 | Coffee Break (Rangos Hall) |
| 16:00 | McConomy Auditorium: Data Compression With Machine Learning. Karen Ullrich, Yibo Yang, Stephan Mandt [video] |
| 16:00 | Connan Room: Online Optimization Meets Federated Learning. Aadirupa Saha, Kumar Kshitij Patel [video] |
| 18:30 | Opening reception (Simmons A/B in Tepper Quad) |
| Time | Event (all in McConomy) |
|---|---|
| 9:00 | Opening remarks (Richard Scheines and Peter Spirtes) |
| 9:05 | Keynote Talk 1: Alexandra Chouldechova. Algorithms in Unjust Systems. (session chair: Peter Spirtes) [video] |
| 10:05 | Coffee Break (Connan Room) |
| 10:30 | Oral Session 1 (Neural networks and Deep Learning) (session chair: Nevin L. Zhang)
10:30 129(v) | MixupE: Understanding and Improving Mixup from Directional Derivative Perspective Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi [slides] [video] 10:55 233 | Neural Probabilistic Logic Programming in Discrete-Continuous Domains Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt [slides] [video] 11:20 402 | An Improved Variational Approximate Posterior for the Deep Wishart Process Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison [slides] [video] 11:45 517 | On Minimizing the Impact of Dataset Shifts on Actionable Explanations Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju [slides] [video] |
| 12:10 | Poster Spotlights 1 (session chair: Nicolas Gisolfi, all spotlights virtual) [video]
91 | Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates Sahra Ghalebikesabi, Christopher C. Holmes, Edwin Fong, Brieuc Lehmann, 190 | Conditional Counterfactual Causal Effect for Individual Attribution Ruiqi Zhao, lei zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Jun Xu, Zhi Geng, Yangbo He, 196 | Random Reshuffling with Variance Reduction: New Analysis and Better Rates Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik, 227 | Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang, 303 | MFA: Multi-scale Feature-aware Attack for Object Detection Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang, 325 | Incentivising Diffusion while Preserving Differential Privacy Fengjuan Jia, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov, 472 | Residual-Based Error Bound for Physics-Informed Neural Networks Shuheng Liu, Xiyue Huang, Pavlos Protopapas, 617 | On the Informativeness of Supervision Signals Ilia Sucholutsky, Ruairidh McLennan Battleday, Katherine M. Collins, Raja Marjieh, Joshua Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths |
| 12:30 | Lunch (boxed lunch, Rangos Hall) |
| 14:00 | Oral Session 2 (Uncertainty quantification and calibration) (session chair: Daniel Andrade)
14:00 482 | Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? Yusuf Sale, Michele Caprio, Eyke Hüllermeier [slides] [video] 14:25 374 | Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier [slides] [video] 14:50 631 | Parity Calibration Youngseog Chung, Aaron Rumack, Chirag Gupta [slides] [video] 15:15 256(v) | Human-in-the-Loop Mixup Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller [slides] [video] |
| 15:40 | Poster Spotlights 2 (session chair: Taposh Banerjee) [video]
virtual: 651 | Adaptivity Complexity for Causal Graph Discovery Davin Choo, Kirankumar Shiragur, 707 | Phase-shifted Adversarial Training Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin, 804 | Fast Proximal Gradient Descent for Support Regularized Sparse Graph Dongfang Sun, Yingzhen Yang, 56 | Stochastic Generative Flow Networks Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio, 654 | Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen, in person 186 | Inference for Mark-Censored Temporal Point Processes Alex James Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth, 643 | Testing Conventional Wisdom (of the Crowd) Noah Burrell, Grant Schoenebeck, 297 | Validation of Composite Systems by Discrepancy Propagation David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn, 480 | Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditional Matthias Scheutz, Michael Jahn. |
| 16:00 | Coffee Break (Connan Room) |
| 16:30 | Poster Session 1 (in-person; Rangos Hall):
8, 35, 88, 100, 136, 143, 219, 220, 236,
247, 251, 281, 288, 290, 307, 328, 332, 356
,368, 370, 375, 387, 392, 394, 396, 406, 407
420, 421, 433, 434, 437, 443, 445, 449,
457, 460, 462, 467, 470, 495, 499, 531,
536, 540, 541, 549, 552, 560, 566, 571, 583, 587, 589, 598, 601, 663, 672, 713, 731, 741,
748, 750, 757, 773, 777, 793
Spotlights: 186, 297, 643, 480 Orals: 95, 233, 249, 374, 402, 482, 486, 517, 597, 631 |
| 19:30 | Close |
| Time | Event (all in McConomy) |
|---|---|
| 9:00 | Oral Session 3 (Causal inference and missing data) (session chair: Negar Kiyavash)
9:00 504(v) | Establishing Markov Equivalence in Cyclic Directed Graphs Tom Claassen, Joris Mooij [slides] [video] 9:25 597 | On Testability and Goodness of Fit Tests in Missing Data Models Razieh Nabi, Rohit Bhattacharya [slides] [video] 9:50 486 | Functional Causal Bayesian Optimization Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa [slides] [video] 10:15 249 | Partial Identification of Dose Responses with Hidden Confounders Myrl G Marmarelis, Greg Ver Steeg, Andrew Jesson, Elizabeth Haddad, Neda Jahanshad, Aram Galstyan [slides] [video] |
| 10:40 | Coffee Break (Connan Room) |
| 11:00 | Poster Session 2 (virtual):
11, 24, 31, 34, 42, 82, 85, 116, 139, 142, 153, 155, 159, 205, 212, 213, 217, 234, 243, 246, 250, 268, 277, 298, 309, 310, 320, 322, 341, 358, 397,
442, 458, 465,
496, 505, 520, 537, 558, 576, 580, 590, 596, 604, 606, 608, 632, 648, 665, 676, 677, 688, 695, 696, 704, 732, 756, 775, 780.
Spotlights: 91, 190, 196, 227, 303, 325, 472, 617, 651, 654, 707, 804. Orals: 129, 256, 380, 447, 504 |
| 12:30 | Lunch (boxed lunch, Rangos Hall) |
| 14:00 | Oral Session 4 (Modelling and learning) (session chair: Gugan Thoppe)
14:00 432 | Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxime Guillaud, Maxim Panov [slides] [video] 14:25 621 | Keep-Alive Caching for the Hawkes process Sushirdeep Narayana, Ian A. Kash [slides] [video] 14:50 701 | Conditional Abstraction Trees for Sample-Efficient Reinforcement Learning Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava [slides] [video] 15:15 380(v) | Provably Efficient Adversarial Imitation Learning with Unknown Transitions Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo [slides] [video] |
| 15:40 | Coffee Break (Connan Room) |
| 16:10 | Keynote Talk 2: Victor Chernozhukov. Long story short: omitted variable bias in causal machine learning (session chair: Ilya Shpitser) [video] |
| 17:10 | Townhall and Best Paper Awards |
| 18:30 | Break and walk to banquet |
| 19:00 | Banquet (Phipps Conservatory and Botanical Gardens) |
| Time | Event (all in McConomy) |
|---|---|
| 9:00 | Keynote Talk 3: Caroline Uhler. Causal Representation Learning and Optimal Intervention Design (session chair: Daniel Malinsky) [video] |
| 10:00 | Coffee Break (Connan Room) |
| 10:30 | Oral Session 5 (Methods for intractable learning problems) (session chair: Yan Yan)
10:30 447(v) | Meta-learning Control Variates: Variance Reduction with Limited Data Zhuo Sun, Chris J. Oates, Francois-Xavier Briol [slides] [video] 10:55 149 | The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference Charles Margossian, Lawrence K. Saul [slides] [video] 11:20 95 | Towards Physically Reliable Molecular Representation Learning Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim, Jaegul Choo, Hongkee Yoon, Joonseok Lee [slides] [video] 11:45 342 | Revisiting Bayesian Network Learning with Small Vertex Cover Juha Harviainen, Mikko Koivisto [slides] [video] |
| 12:10 | Poster Spotlights 3 (session chair: Jakob Runge) [video]
79 | BISCUIT: Causal Representation Learning from Binary Interactions Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves, 257 | Composing Efficient, Robust Tests for Policy Selection Dustin Morrill, Thomas Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone, 294 | Nyström $M$-Hilbert-Schmidt Independence Criterion Florian Kalinke, Zoltán Szabó, 354 | JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Koethe, Paul Buerkner, 476 | Causal Inference With Outcome-Dependent Missingness And Self-Censoring Jacob Morris Chen, Daniel Malinsky, Rohit Bhattacharya, 516 | CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models Jiazheng Li, ZHAOYUE SUN, Bin Liang, Lin Gui, Yulan He, 556 | Fast and Scalable Score-Based Kernel Calibration Tests Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton, 559 | "Private Prediction Strikes Back!" Private Kernelized Nearest Neighbors with Individual R\'{e}nyi Filter Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang. |
| 12:30 | Lunch (boxed lunch, Rangos Hall) |
| 14:00 | Oral Session 6 (Probabilistic circuit models) (session chair: Rina Dechter)
14:00 430 | Local Message Passing on Frustrated Systems Luca Schmid, Joshua Brenk, Laurent Schmalen [slides] [video] 14:25 353 | On Inference and Learning With Probabilistic Generating Circuits Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto [slides] [video] 14:50 526 | Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan [slides] [video] 15:15 118 | Probabilistic Circuits That Know What They Don’t Know Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting [slides] [video] |
| 15:40 | Poster Spotlights 4 (session chair: Christopher Quinn) [video]
41 | Inference and Sampling of Point Processes from Diffusion Excursions Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh, 261 | Studying the Effect of GNN Spatial Convolutions On The Embedding Space's Geometry Claire Donnat, So Won Jeong, 747 | A Decoder Suffices for Query-Adaptive Variational Inference Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth, 658 | Aligned Diffusion Schrödinger Bridges Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, Charlotte Bunne, 611 | Bayesian Inference Approach for Entropy Regularized Reinforcement Learning with Stochastic Dynamics Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V Kulkarni, 797 | Inference for Probabilistic Dependency Graphs Oliver Ethan Richardson, Joseph Halpern, Christopher De Sa, 764 | Robust Gaussian Process Regression with the Trimmed Marginal Likelihood Daniel Andrade, Akiko Takeda. |
| 16:00 | Coffee Break (Connan Room) |
| 16:30 | Poster Session 3 (in-person; Rangos Hall):
39, 40, 45, 46, 47, 67, 80, 84, 105,
114, 127, 130, 138, 144, 147, 150, 184, 189,
211, 214, 216, 235, 240, 255, 272, 273, 306,
351, 390, 466, 477, 494, 500, 503, 507,
521, 524, 527, 529, 530, 551, 554, 555, 561, 567,
579, 584, 594, 595, 605, 607, 636, 644,
646, 657, 669, 679, 722, 753, 761, 789.
Spotlights: 41, 79, 257, 261, 294, 354, 476, 516, 556, 559, 611, 658, 747, 764, 797. 118, 149, 342, 353, 430, 432, 526, 621, 701 |
| 19:30 | Close |
Causal inference for time series data (Room: Studio Theater)
Epistemic Uncertainty in Artificial Intelligence (Room: Connan Room)
The History and Development of Search Methods for Causal Structure (Room: McConomy Auditorium)
Tractable Probabilistic Modeling (Room: McKenna, Peter, and Wright Room)
| Time | Event |
|---|---|
| 9:00 | Session 1 |
| 10:00 | Coffee (Rangos 3) |
| 10:30 | Session 2 |
| 12:30 | Lunch (boxed lunch) |
| 14:00 | Session 3 |
| 16:00 | Coffee (informal) |
| 16:30 | Session 4 |
| 18:30 | Close |
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