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TUE 13 APR

8:45 a.m.

9 a.m.

Invited Talk:

Emmanuel Candes

(ends 10:15 AM)

10:30 a.m.

Oral
s
10:30-11:30

[10:30]
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent

[10:45]
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models

[11:00]
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

[11:15]
Stable ResNet

(ends 11:30 AM)

11:30 a.m.

Oral
s
11:30-12:30

[11:30]
Couplings for Multinomial Hamiltonian Monte Carlo

[11:45]
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo

[12:00]
Maximal Couplings of the Metropolis-Hastings Algorithm

[12:15]
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences

(ends 12:30 PM)

12:30 p.m.

2 p.m.

(ends 4:00 PM)

4:15 p.m.

Oral
s
4:15-5:15

[4:15]
Federated Multi-armed Bandits with Personalization

[4:30]
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning

[4:45]
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

[5:00]
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective

(ends 5:15 PM)

5:15 p.m.

Oral
s
5:15-6:15

[5:15]
Entropy Partial Transport with Tree Metrics: Theory and Practice

[5:30]
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process

[5:45]
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?

[6:00]
A Variational Information Bottleneck Approach to Multi-Omics Data Integration

(ends 6:15 PM)

6:30 p.m.

(ends 8:30 PM)

8:30 p.m.

WED 14 APR

6 a.m.

(ends 8:00 AM)

8:15 a.m.

Oral
s
8:15-9:15

[8:15]
Neural Enhanced Belief Propagation on Factor Graphs

[8:30]
An Analysis of LIME for Text Data

[8:45]
Bandit algorithms: Letting go of logarithmic regret for statistical robustness

[9:00]
The Sample Complexity of Level Set Approximation

(ends 9:15 AM)

9:15 a.m.

Oral
s
9:15-10:15

[9:15]
Logistic Q-Learning

[9:30]
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits

[9:45]
Robust and Private Learning of Halfspaces

[10:00]
Hadamard Wirtinger Flow for Sparse Phase Retrieval

(ends 10:15 AM)

10:30 a.m.

Oral
s
10:30-11:30

[10:30]
Projection-Free Optimization on Uniformly Convex Sets

[10:45]
Measure Transport with Kernel Stein Discrepancy

[11:00]
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

[11:15]
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

(ends 11:30 AM)

11:30 a.m.

Oral
s
11:30-12:30

[11:30]
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model

[11:45]
Matérn Gaussian Processes on Graphs

[12:00]
Differentially Private Analysis on Graph Streams

[12:15]
On Learning Continuous Pairwise Markov Random Fields

(ends 12:30 PM)

12:45 p.m.

(ends 2:45 PM)

3 p.m.

4 p.m.

Invited Talk:

Bin Yu

(ends 5:15 PM)

5:15 p.m.

5:30 p.m.

THU 15 APR

4 a.m.

6 a.m.

7:30 a.m.

(ends 9:30 AM)

9:45 a.m.

noon

Oral
s
12:00-1:00

[12:00]
Private optimization without constraint violations

[12:15]
Learning Smooth and Fair Representations

[12:30]
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration

[12:45]
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

(ends 1:00 PM)

1 p.m.

Oral
s
1:00-2:00

[1:00]
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

[1:15]
Does Invariant Risk Minimization Capture Invariance?

[1:30]
Density of States Estimation for Out of Distribution Detection

[1:45]
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time

(ends 2:00 PM)

2:15 p.m.

Oral
s
2:15-3:15

[2:15]
Sketch based Memory for Neural Networks

[2:30]
Associative Convolutional Layers

[2:45]
Deep Fourier Kernel for Self-Attentive Point Processes

[3:00]
Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression

(ends 3:15 PM)

3:15 p.m.

Oral
s
3:15-4:15

[3:15]
A constrained risk inequality for general losses

[3:30]
Misspecification in Prediction Problems and Robustness via Improper Learning

[3:45]
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs

[4:00]
Faster Kernel Interpolation for Gaussian Processes

(ends 4:15 PM)