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AISTATS 2021 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

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