UAI 2019 - Program Schedule

July 22nd: Tutorials

Time Event
8:30am - 10:30am Tutorial 1: Tractable Probabilistic Models: Representations, Algorithms, Learning, and Applications
10:30am - 12:30pm Tutorial 2: Mixing Graphical Models and Neural Nets Like Chocolate and Peanut Butter
12:30pm - 2:00pmLunch break
2:00pm - 4:00pm Tutorial 3: Causality and Reinforcement Learning
4:00pm - 6:00pm Tutorial 4: Mathematics of Deep Learning

July 23rd: Main Conference

Time Event
7:30am - 8:40amRegistration
8:40am - 9:00amOpening words
9:00am - 10:00amInvited talk: Rina Dechter
10:00am - 10:20amCoffee break
10:20am - 10:40amOral session: Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
10:40am - 11:00amOral session: Generating and Sampling Orbits for Lifted Probabilistic Inference
11:00am - 11:20amOral session: Conditional Expectation Propagation
11:20am - 11:40amOral session: Belief Propagation: Accurate Marginals or Accurate Partition Function -- Where is the Difference?
11:40am - 12:00pmOral session: Sliced Score Matching
12:00pm - 1:30pmLunch break
1:30pm - 2:30pmInvited talk: Suchi Saria
2:30pm - 2:50pmOral session: Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
2:50pm - 3:20pmSpotlights
3:20pm - 3:40pmCoffee break
3:40pm - 4:00pmOral session: Towards a Better Understanding and Regularization of the GAN Training Dynamics
4:00pm - 4:20pmOral session: Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
4:20pm - 4:40pmOral session: Scaling Tight Relaxations for Neural Network Verification
4:40pm - 5:00pmOral session: Sinkhorn AutoEncoders
5:00pm - 5:20pmOral session: Learning with Non-Convex Truncated Losses by SGD
5:20pm - 5:50pmSpotlights
6:00pm - 9:00pmPoster session

July 24th: Main Conference

Time Event
8:40am - 9:40amInvited talk: Stefanie Jegelka
9:40am - 10:00amOral session: General Identifiability with Arbitrary Surrogate Experiments
10:00am - 10:20amCoffee break
10:20am - 10:40amOral session: On Open-Universe Causal Reasoning
10:40am - 11:00amOral session: Approximate Causal Abstractions
11:00am - 11:20amOral session: Causal Constraints Models
11:20am - 11:40amOral session: Exclusivity Graph Approach to Instrumental Inequalities
11:40am - 12:00pmOral session: Finding Minimal d-separators in Linear Time and Applications
12:00pm - 1:30pmLunch break
1:30pm - 2:30pmInvited talk: Emma Brunskill
2:30pm - 2:50pmOral session: Off-Policy Policy Gradient with Stationary Distribution Correction
2:50pm - 3:10pmOral session: Wasserstein Fair Classification
3:20pm - 3:40pmCoffee break
3:40pm - 4:00pmOral session: A Fast Proximal Point Method for Computing Exact Wasserstein Distance
4:30pm - 9:00pmBanquet

July 25th: Main Conference

Time Event
8:40am - 9:40amInvited talk: Yee Whey Teh
9:40am - 10:00amOral session: Exact Sampling of Directed Acyclic Graphs from Modular Distributions
10:00am - 10:20amCoffee break
10:20am - 10:40amOral session: Bayesian Optimization with Binary Auxiliary Information
10:40am - 11:00amOral session: Perturbed-History Exploration in Stochastic Linear Bandits
11:00am - 11:20amOral session: Cascade Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank
11:20am - 11:40amOral session: A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
11:40am - 12:00pmOral session: On the Relationship Between Satisfiability and Markov Decision Processes
12:00pm - 1:30pmLunch break
1:30pm - 1:50pmOral session: A Bayesian Approach to Robust Reinforcement Learning
1:50pm - 2:10pmOral session: A Sharp Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
2:10pm - 2:30pmOral session: Co-Training for Policy Learning
2:30pm - 2:50pmOral session: Truly Proximal Policy Optimization
2:50pm - 3:20pmSpotlights
3:20pm - 3:40pmCoffee break
3:40pm - 4:00pmOral session: Correlated Fictitious Play for Aggregation Systems
4:00pm - 4:20pmOral session: Coordinating Users of Shared Facilities via Predictive Assistants: Algorithms and Game-theoretic Analysis
4:20pm - 4:40pmOral session: Interpreting Humans Literally is More Robust for Objective Learning
4:40pm - 5:00pmOral session: Randomized Iterative Algorithms for Fisher Discriminant Analysis
5:00pm - 5:20pmOral session: Countdown Regression: Sharp and Calibrated Survival Predictions
5:20pm - 5:50pmSpotlights
6:00pm - 9:00pmPoster session

Poster Session - July 23rd - Tuesday

Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data
A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
One-Shot Inference in Markov Random Fields
Learning Factored Markov Decision Processes with Unawareness
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
Comparing EM with GD in Mixture Models of Two Components
Efficient Search-Based Weighted Model Integration
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA
The Incomplete Rosetta Stone problem: Identifiability Results for Multi-View Nonlinear ICA
Random Clique Covers for Graphs with Local Density and Global Sparsity
Fall of Emperor: Breaking Defenses of Byzantine-tolerant SGD
Domain Generalization via Multi-Domain Discriminant Analysis
Towards Robust Relational Causal Discovery
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem
Randomized Value Functions via Multiplicative Normalizing Flows
Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Augmenting and Tuning Knowledge Graph Embeddings
A Tighter Analysis of Randomised Policy Iteration
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Probabilistic Programming for Birth-Death Models of Evolution using an Alive Particle Filter with Delayed Sampling
Active Multi-Information Source Bayesian Quadrature
Efficient Multitask Feature and Relationship Learning
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Sampling-Free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging
Guaranteed Scalable Learning of Latent Tree Models
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Reliable Uncertainty Estimates in Neural Networks using Noise Contrastive Priors
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations
Intervening on Network Ties
Causal Inference Under Interference and Network Uncertainty
Learnability for the Information Bottleneck
Learning Belief Representations for Imitation Learning in POMDPs
Conditional Mutual Information Estimation with Applications to Conditional Independence Testing
Almost Matching Exactly With Instrumental Variables
Low Frequency Adversarial Perturbation
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption
Subspace Inference for Bayesian Deep Learning
Variational Inference of Penalized Regression with Submodular Functions
Embarrassingly Parallel MCMC using Deep Invertible Transformations
Fast Proximal Gradient Descent for Non-Convex Optimization
Block Neural Autoregressive Flow

Poster Session - July 25th - Thursday

On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function
Variational Regret Bounds for Reinforcement Learning
Reducing Exploration of Dying Arms in Mortal Bandits
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Regular LDPC Construction for Sparse Hashing
Efficient Planning Under Uncertainty with Incremental Refinement
Cubic Regularization with Momentum for Nonconvex Optimization
Stability of Linear Structural Equation Models of Causal Inference
The Role of Memory in Stochastic Optimization
Random Search and Reproducibility for Neural Architecture Search
Joint Nonparametric Precision Matrix Estimation with Confounding
Approximate Inference in Structured Instances with Noisy Categorical Observations
Fisher-Bures Adversary Graph Convolutional Networks
Periodic Kernel Approximation by Index Set Fourier Series Features
Deep Mixture of Experts via Shallow Embedding
MAB with Graphs: A Graph Coloring Approach for Regret Minimization
A Universal Algorithm for Online Convex Optimization
Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones
Variational Sparse Coding
How to Exploit Structure while Solving Weighted Model Integration Problems
Practical Multi-Fidelity Bayesian Optimization of Iterative Machine Learning Algorithms
Online Factorization and Partition of Complex Networks by Random Walk
On Densification for Minwise Hashing
N-GCN: Multi-scale Graph Convolution for Semi-Supervised Node Classification
Problem-dependent Regret Bounds for Online Learning with Feedback Graph
Variational Training for Large-Scale Noisy-Or Bayesian Networks
Fake It Till You Make It: Learning-Compatible Performance Support
Convergence Analysis of Gradient-Based Learning in Continuous Games
Approximate Relative Value Learning for Average-Reward Continuous state MDPs
Real-Time Robotic Search using Hierarchical Spatial Point Processes
Learning Intervention for True News Diffusion to Combat Fake News Spread using Deep Reinforcement Learning
P3O: Policy-On Policy-Off Policy Optimization
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Object Conditioning for Causal Inference
Parametric Mechanism Design under Uncertainty
Identification In Missing Data Models Represented By Directed Acyclic Graphs
A Weighted Mini-Bucket Bound for Solving Influence Diagram
Probability Distillation: A Caveat and Alternatives

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