UAI 2024 - Schedule

Time	Monday	Tuesday	Wednesday	Thursday	Friday
8:15	Registration opens	Registration opens
9:00	Tutorials 1 and 2	Keynote 1 Nicolò Cesa-Bianchi	Keynote 2 Dominik Janzing	Keynote 3 Vanessa Didelez	Workshops 1, 2, 3
9:30		Keynote 1 Nicolò Cesa-Bianchi	Keynote 2 Dominik Janzing	Keynote 3 Vanessa Didelez	Workshops 1, 2, 3
10:00		Coffee	Coffee	Coffee	Coffee
10:30		Oral Session 1 Deep Learning	Oral Session 3 Causality	Oral Session 5 Learning Algorithms	Workshops 1, 2, 3
11:00	Coffee
11:30	Tutorials 1 and 2
12:00	Tutorials 1 and 2
12:30	Lunch	Lunch	Lunch	Lunch	Lunch
13:00	Lunch
13:30	Tutorials 3 and 4
14:00		Oral Session 2 Statistical Inference	Poster session 2 (virtual)	Oral Session 6 Probabilistic Modeling & Decision Making	Workshops 1, 2, 3
14:30
15:00
15:30		Spotlights Session 1	Coffee	Spotlights Session 2
16:00	Coffee	Coffee	Oral Session 2 Sequential Learning	Coffee	Coffee
16:30	Tutorial 5	Poster Session 1		Poster Session 3	Workshops 1, 2, 3
17:00
17:30
18:00			Town Hall
18:30			Town Hall
19:00
19:30
20:00	Reception 20:00 - 22:00		Banquet 20:00 - 23:00

Monday 15th July

Time	Event
9:00	Tutorial 1: An introduction to Performative Prediction
9:00	Tutorial 2: Causal Graphical Methods For Handling Nonignorable Missing Data
11:00	Coffee Break
11:30	Continuation of tutorials
12:30	Lunch
13:30	Tutorial 3: Distribution-Free Predictive Uncertainty Quantification: Strengths and Limits of Conformal Prediction
13:30	Tutorial 4: Geometric Probabilistic Models
16:00	Coffee Break
16:30 - 19:30	Tutorial 5: Recent Advances of Statistical Reinforcement Learning
20:00 - 22:00	Reception

Time	Event
9:00	Keynote Talk 1
10:00	Coffee Break
10:30	Oral Session 1: Deep Learning 214 \| Functional Wasserstein Bridge Inference for Bayesian Deep Learning 224 \| Reflected Schr\"odinger Bridge for Constrained Generative Modeling" 402 \| Pix2Code: Learning to Compose Neural Visual Concepts as Programs 431 \| Normalizing Flows for Conformal Regression 570 \| Understanding Pathologies of Deep Heteroskedastic Regression
12:30	Lunch
14:00	Oral Session 2: Statistical Inference 20 \| Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls 101 \| Inference for Optimal Linear Treatment Regimes in Personalized Decision-making 198 \| Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable 233 \| Statistical and Causal Robustness for Causal Null Hypothesis Tests
15:30	Spotlights Session 1 215 \| Approximate Bayesian Computation with Path Signatures 361 \| Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression 386 \| Faster Perfect Sampling of Bayesian Network Structures 454 \| Extremely Greedy Equivalence Search 549 \| Common Event Tethering to Improve Prediction of Rare Clinical Events 583 \| Hidden Population Estimation with Indirect Inference and Auxiliary Information
16:00	Coffee Break
16:30	Poster Session 1 (in-person)
19:30	Close

Time	Event
9:00	Keynote Talk 2
10:00	Coffee Break
10:30	Oral Session 3: Causality 108 \| Towards Bounding Causal Effects under Markov Equivalence 409 \| Targeted Reduction of Causal Models 457 \| Characterising Interventions in Causal Games 500 \| Causally Abstracted Multi-armed Bandits 575 \| A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
12:30	Lunch
14:00	Poster Session 2 (virtual)
15:30	Coffee Break
16:00	Oral Session 2: Sequential Learning 65 \| Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction 76 \| Towards Minimax Optimality of Model-based Robust Reinforcement Learning 145 \| Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes 499 \| Group Fairness in Predict-Then-Optimize Settings for Restless Bandits 609 \| Recursively-Constrained Partially Observable Markov Decision Processes
18:10 - 19:00	Town Hall
20:00 - 23:00	Banquet

Time	Event
9:00	Keynote Talk 3
10:00	Coffee Break
10:30	Oral Session 5: Learning Algorithms 176 \| Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection 497 \| Learning Accurate and Interpretable Decision Trees 511 \| A Graph Theoretic Approach for Preference Learning with Feature Information 545 \| Bayesian Active Learning in the Presence of Nuisance Parameters 571 \| Support Recovery in Sparse PCA with General Missing Data
12:30	Lunch
14:00	Oral Session 6: Probabilistic Modeling & Decision Making 42 \| Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization 182 \| On the Capacitated Facility Location Problem with Scarce Resources 464 \| Generalized Expected Utility as a Universal Decision Rule -- A Step Forward 739 \| Polynomial Semantics of Tractable Probabilistic Circuits
15:30	Spotlights Session 2 25 \| Graph Feedback Bandits with Similar Arms 317 \| Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise 360 \| Convergence Behavior of an Adversarial Weak Supervision Method 406 \| The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data 691 \| BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts 696 \| Walking the Values in Bayesian Inverse Reinforcement Learning
16:00	Coffee Break
16:30	Poster Session 3 (in-person)
19:30	Close

Time	Event
9:00	Workshop 1: Causal Inference
9:00	Workshop 2: Causal Inference for Time Series Data
9:00	Workshop 3: Tractable Probabilistic Modeling: Models We Trust
10:00	Coffee Break
10:30	Continuation of workshops
12:30	Lunch
14:00	Continuation of workshops
16:00	Coffee Break
16:30	Continuation of workshops
18:30	Close

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