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oral.html
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---
layout: default
title: Oral Sessions
hide: true
weight: 0
---
<h1>{{ site.conference.short_name }} {{ site.conference.year }} Oral Sessions Schedule</h1>
All times are in Pacific Daylight Time (PDT).
<br>
<br>
<!-- ^([A-Za-z -:]+)\t([A-Za-z 0-9:-]+)$ -->
<!-- <td markdown="span">$1</td>\n<td markdown="span">$2</td>\n -->
<details>
<summary>Click to expand schedule for Day 1 Oral Sessions (Tue, April 13)</summary>
<table style="float: left" border="0" cellpadding="2" cellspacing="0" width="95%">
<colgroup>
<col width="25%" />
<col width="50%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header" style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span"><b>Session Title</b></td>
<td markdown="span"><b>Title</b></td>
<td markdown="span"><b>Session</b></td>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="4">Theory of Statistical and Deep Learning Methods</th>
<td markdown="span">Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent</td>
<td markdown="span">Session 1: April 13 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<td markdown="span">Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models</td>
<td markdown="span">Session 1: April 13 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<td markdown="span">Towards a Theoretical Understanding of the Robustness of Variational Autoencoders</td>
<td markdown="span">Session 1: April 13 at 10:30am-11:30am PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Stable ResNet</td>
<td markdown="span">Session 1: April 13 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<th rowspan="4">Sampling Methods</th>
<td markdown="span">Couplings for Multinomial Hamiltonian Monte Carlo</td>
<td markdown="span">Session 2: April 13 at 11:30am-12:30pm PDT</td>
</tr>
<tr>
<td markdown="span">An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo</td>
<td markdown="span">Session 2: April 13 at 11:30am-12:30pm PDT</td>
</tr>
<tr>
<td markdown="span">Maximal Couplings of the Metropolis-Hastings Algorithm</td>
<td markdown="span">Session 2: April 13 at 11:30am-12:30pm PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences</td>
<td markdown="span">Session 2: April 13 at 11:30am-12:30pm PDT</td>
</tr>
<tr>
<th rowspan="4">Bandits, Reinforcement Learning / Optimization</th>
<td markdown="span">Federated Multi-armed Bandits with Personalization</td>
<td markdown="span">Session 3: April 13 at 16:15-17:15 PDT</td>
</tr>
<tr>
<td markdown="span">Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning</td>
<td markdown="span">Session 3: April 13 at 16:15-17:15 PDT</td>
</tr>
<tr>
<td markdown="span">Provably Efficient Safe Exploration via Primal-Dual Policy Optimization</td>
<td markdown="span">Session 3: April 13 at 16:15-17:15 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective</td>
<td markdown="span">Session 3: April 13 at 16:15-17:15 PDT</td>
</tr>
<tr>
<th rowspan="4" style="border-bottom-style: solid; border-bottom: solid #696969; ">Theory and Practice of Machine Learning</th>
<td markdown="span">Entropy Partial Transport with Tree Metrics: Theory and Practice</td>
<td markdown="span">Session 4: April 13 at 17:15-18:15 PDT</td>
</tr>
<tr>
<td markdown="span">Independent Innovation Analysis for Nonlinear Vector Autoregressive Process</td>
<td markdown="span">Session 4: April 13 at 17:15-18:15 PDT</td>
</tr>
<tr>
<td markdown="span">Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
<td markdown="span">Session 4: April 13 at 17:15-18:15 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">A Variational Information Bottleneck Approach to Multi-Omics Data Integration</td>
<td markdown="span">Session 4: April 13 at 17:15-18:15 PDT</td>
</tr>
</tbody>
</table>
</details>
<br>
<details>
<summary>Click to expand schedule for Day 2 Oral Sessions (Wed, April 14)</summary>
<table style="float: left" border="0" cellpadding="2" cellspacing="0" width="95%">
<colgroup>
<col width="25%" />
<col width="50%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header" style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span"><b>Session Title</b></td>
<td markdown="span"><b>Title</b></td>
<td markdown="span"><b>Session</b></td>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="4">Theory and Methods of Learning</th>
<td markdown="span">Neural Enhanced Belief Propagation on Factor Graphs</td>
<td markdown="span">Session 5: April 14 at 08:15am-9:15am PDT</td>
</tr>
<tr>
<td markdown="span">An Analysis of LIME for Text Data</td>
<td markdown="span">Session 5: April 14 at 08:15am-9:15am PDT</td>
</tr>
<tr>
<td markdown="span">Bandit algorithms: Letting go of logarithmic regret for statistical robustness</td>
<td markdown="span">Session 5: April 14 at 08:15am-9:15am PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">The Sample Complexity of Level Set Approximation</td>
<td markdown="span">Session 5: April 14 at 08:15am-9:15am PDT</td>
</tr>
<tr>
<th rowspan="4">Bandits, Reinforcement Learning / Learning Theory / Sparse Methods</th>
<td markdown="span">Logistic Q-Learning</td>
<td markdown="span">Session 6: April 14 at 9:15am-10:15am PDT</td>
</tr>
<tr>
<td markdown="span">Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits</td>
<td markdown="span">Session 6: April 14 at 9:15am-10:15am PDT</td>
</tr>
<tr>
<td markdown="span">Robust and Private Learning of Halfspaces</td>
<td markdown="span">Session 6: April 14 at 9:15am-10:15am PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Hadamard Wirtinger Flow for Sparse Phase Retrieval</td>
<td markdown="span">Session 6: April 14 at 9:15am-10:15am PDT</td>
</tr>
<tr>
<th rowspan="4">Optimization / Learning Theory / Generalization</th>
<td markdown="span">Projection-Free Optimization on Uniformly Convex Sets</td>
<td markdown="span">Session 7: April 14 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<td markdown="span">Measure Transport with Kernel Stein Discrepancy</td>
<td markdown="span">Session 7: April 14 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<td markdown="span">Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization</td>
<td markdown="span">Session 7: April 14 at 10:30am-11:30am PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Improving Adversarial Robustness via Unlabeled Out-of-Domain Data</td>
<td markdown="span">Session 7: April 14 at 10:30am-11:30am PDT</td>
</tr>
<tr>
<th rowspan="4" style="border-bottom-style: solid; border-bottom: solid #696969; ">Graphs and Networks</th>
<td markdown="span">Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model</td>
<td markdown="span">Session 8: April 14 at 11:30am-12:30pm PDT</td>
</tr>
<tr>
<td markdown="span">Matérn Gaussian Processes on Graphs</td>
<td markdown="span">Session 8: April 14 at 11:30am-12:30pm PDT</td>
</tr>
<tr>
<td markdown="span">Differentially Private Analysis on Graph Streams</td>
<td markdown="span">Session 8: April 14 at 11:30am-12:30pm PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">On Learning Continuous Pairwise Markov Random Fields</td>
<td markdown="span">Session 8: April 14 at 11:30am-12:30pm PDT</td>
</tr>
</tbody>
</table>
</details>
<br>
<details>
<summary>Click to expand schedule for Day 3 Oral Sessions (Thu, April 15)</summary>
<table style="float: left" border="0" cellpadding="2" cellspacing="0" width="95%">
<colgroup>
<col width="25%" />
<col width="50%" />
<col width="25%" />
</colgroup>
<thead>
<tr class="header" style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span"><b>Session Title</b></td>
<td markdown="span"><b>Title</b></td>
<td markdown="span"><b>Session</b></td>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="4"> Fairness / Privacy / Decision Making / Data Cleaning </th>
<td markdown="span"> Private optimization without constraint violations </td>
<td markdown="span">Session 9: April 15 at 12:00-13:00 PDT</td>
</tr>
<tr>
<td markdown="span">Learning Smooth and Fair Representations</td>
<td markdown="span">Session 9: April 15 at 12:00-13:00 PDT</td>
</tr>
<tr>
<td markdown="span">Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration</td>
<td markdown="span">Session 9: April 15 at 12:00-13:00 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">PClean: Bayesian Data Cleaning at Scale via Domain-Specific Probabilistic Programming</td>
<td markdown="span">Session 9: April 15 at 12:00-13:00 PDT</td>
</tr>
<tr>
<th rowspan="4"> Generalization / Reinforcement Learning / Optimization </th>
<td markdown="span">Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders</td>
<td markdown="span">Session 10: April 15 at 13:00-14:00 PDT</td>
</tr>
<tr>
<td markdown="span">Does Invariant Risk Minimization Capture Invariance?</td>
<td markdown="span">Session 10: April 15 at 13:00-14:00 PDT</td>
</tr>
<tr>
<td markdown="span">Density of States Estimation for Out of Distribution Detection</td>
<td markdown="span">Session 10: April 15 at 13:00-14:00 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time</td>
<td markdown="span">Session 10: April 15 at 13:00-14:00 PDT</td>
</tr>
<tr>
<th rowspan="4"> Deep Learning / High-dimensionality </th>
<td markdown="span">Sketch based Memory for Neural Networks</td>
<td markdown="span">Session 11: April 15 at 14:15-15:15 PDT</td>
</tr>
<tr>
<td markdown="span">Associative Convolutional Layers</td>
<td markdown="span">Session 11: April 15 at 14:15-15:15 PDT</td>
</tr>
<tr>
<td markdown="span">Deep Fourier kernel for self-attentive point processes</td>
<td markdown="span">Session 11: April 15 at 14:15-15:15 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Uniform consistency of cross-validation estimators for high-dimensional ridge regression</td>
<td markdown="span">Session 11: April 15 at 14:15-15:15 PDT</td>
</tr>
<tr>
<th rowspan="4" style="border-bottom-style: solid; border-bottom: solid #696969; "> Learning Theory </th>
<td markdown="span">A constrained risk inequality for general losses</td>
<td markdown="span">Session 12: April 15 at 15:15-16:15 PDT</td>
</tr>
<tr>
<td markdown="span">Misspecification in Prediction Problems and Robustness via Improper Learning</td>
<td markdown="span">Session 12: April 15 at 15:15-16:15 PDT</td>
</tr>
<tr>
<td markdown="span">Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs</td>
<td markdown="span">Session 12: April 15 at 15:15-16:15 PDT</td>
</tr>
<tr style="border-bottom-style: solid; border-bottom: solid #696969; ">
<td markdown="span">Faster Kernel Interpolation for Gaussian Processes</td>
<td markdown="span">Session 12: April 15 at 15:15-16:15 PDT</td>
</tr>
</tbody>
</table>
</details>
<br>