Oral Sessions Schedule

All times are in Pacific Daylight Time (PDT).

Click to expand schedule for Day 1 Oral Sessions (Tue, April 13)

Session Title	Title	Session
Theory of Statistical and Deep Learning Methods	Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent	Session 1: April 13 at 10:30am-11:30am PDT
	Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models	Session 1: April 13 at 10:30am-11:30am PDT
	Towards a Theoretical Understanding of the Robustness of Variational Autoencoders	Session 1: April 13 at 10:30am-11:30am PDT
	Stable ResNet	Session 1: April 13 at 10:30am-11:30am PDT
Sampling Methods	Couplings for Multinomial Hamiltonian Monte Carlo	Session 2: April 13 at 11:30am-12:30pm PDT
	An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo	Session 2: April 13 at 11:30am-12:30pm PDT
	Maximal Couplings of the Metropolis-Hastings Algorithm	Session 2: April 13 at 11:30am-12:30pm PDT
	GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences	Session 2: April 13 at 11:30am-12:30pm PDT
Bandits, Reinforcement Learning / Optimization	Federated Multi-armed Bandits with Personalization	Session 3: April 13 at 16:15-17:15 PDT
	Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning	Session 3: April 13 at 16:15-17:15 PDT
	Provably Efficient Safe Exploration via Primal-Dual Policy Optimization	Session 3: April 13 at 16:15-17:15 PDT
	Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective	Session 3: April 13 at 16:15-17:15 PDT
Theory and Practice of Machine Learning	Entropy Partial Transport with Tree Metrics: Theory and Practice	Session 4: April 13 at 17:15-18:15 PDT
	Independent Innovation Analysis for Nonlinear Vector Autoregressive Process	Session 4: April 13 at 17:15-18:15 PDT
	Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?	Session 4: April 13 at 17:15-18:15 PDT
	A Variational Information Bottleneck Approach to Multi-Omics Data Integration	Session 4: April 13 at 17:15-18:15 PDT

Click to expand schedule for Day 2 Oral Sessions (Wed, April 14)

Session Title	Title	Session
Theory and Methods of Learning	Neural Enhanced Belief Propagation on Factor Graphs	Session 5: April 14 at 08:15am-9:15am PDT
	An Analysis of LIME for Text Data	Session 5: April 14 at 08:15am-9:15am PDT
	Bandit algorithms: Letting go of logarithmic regret for statistical robustness	Session 5: April 14 at 08:15am-9:15am PDT
	The Sample Complexity of Level Set Approximation	Session 5: April 14 at 08:15am-9:15am PDT
Bandits, Reinforcement Learning / Learning Theory / Sparse Methods	Logistic Q-Learning	Session 6: April 14 at 9:15am-10:15am PDT
	Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits	Session 6: April 14 at 9:15am-10:15am PDT
	Robust and Private Learning of Halfspaces	Session 6: April 14 at 9:15am-10:15am PDT
	Hadamard Wirtinger Flow for Sparse Phase Retrieval	Session 6: April 14 at 9:15am-10:15am PDT
Optimization / Learning Theory / Generalization	Projection-Free Optimization on Uniformly Convex Sets	Session 7: April 14 at 10:30am-11:30am PDT
	Measure Transport with Kernel Stein Discrepancy	Session 7: April 14 at 10:30am-11:30am PDT
	Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization	Session 7: April 14 at 10:30am-11:30am PDT
	Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Session 7: April 14 at 10:30am-11:30am PDT
Graphs and Networks	Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model	Session 8: April 14 at 11:30am-12:30pm PDT
	Matérn Gaussian Processes on Graphs	Session 8: April 14 at 11:30am-12:30pm PDT
	Differentially Private Analysis on Graph Streams	Session 8: April 14 at 11:30am-12:30pm PDT
	On Learning Continuous Pairwise Markov Random Fields	Session 8: April 14 at 11:30am-12:30pm PDT

Click to expand schedule for Day 3 Oral Sessions (Thu, April 15)

Session Title	Title	Session
Fairness / Privacy / Decision Making / Data Cleaning	Private optimization without constraint violations	Session 9: April 15 at 12:00-13:00 PDT
	Learning Smooth and Fair Representations	Session 9: April 15 at 12:00-13:00 PDT
	Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration	Session 9: April 15 at 12:00-13:00 PDT
	PClean: Bayesian Data Cleaning at Scale via Domain-Specific Probabilistic Programming	Session 9: April 15 at 12:00-13:00 PDT
Generalization / Reinforcement Learning / Optimization	Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders	Session 10: April 15 at 13:00-14:00 PDT
	Does Invariant Risk Minimization Capture Invariance?	Session 10: April 15 at 13:00-14:00 PDT
	Density of States Estimation for Out of Distribution Detection	Session 10: April 15 at 13:00-14:00 PDT
	Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time	Session 10: April 15 at 13:00-14:00 PDT
Deep Learning / High-dimensionality	Sketch based Memory for Neural Networks	Session 11: April 15 at 14:15-15:15 PDT
	Associative Convolutional Layers	Session 11: April 15 at 14:15-15:15 PDT
	Deep Fourier kernel for self-attentive point processes	Session 11: April 15 at 14:15-15:15 PDT
	Uniform consistency of cross-validation estimators for high-dimensional ridge regression	Session 11: April 15 at 14:15-15:15 PDT
Learning Theory	A constrained risk inequality for general losses	Session 12: April 15 at 15:15-16:15 PDT
	Misspecification in Prediction Problems and Robustness via Improper Learning	Session 12: April 15 at 15:15-16:15 PDT
	Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs	Session 12: April 15 at 15:15-16:15 PDT
	Faster Kernel Interpolation for Gaussian Processes	Session 12: April 15 at 15:15-16:15 PDT