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MON 28 MAR

midnight

1:30 a.m.

Oral
s
1:30-2:30

[1:30]
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients

[1:45]
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably

[2:00]
Survival regression with proper scoring rules and monotonic neural networks

[2:15]
Multivariate Quantile Function Forecaster

(ends 2:30 AM)

2:30 a.m.

Oral
s
2:30-3:30

[2:30]
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion

[2:45]
Nonparametric Relational Models with Superrectangulation

[3:00]
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

[3:15]
Unifying Importance Based Regularisation Methods for Continual Learning

(ends 3:30 AM)

4:30 a.m.

(ends 6:00 AM)

6 a.m.

Oral
s
6:00-7:00

[6:00]
Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions

[6:15]
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction

[6:30]
On the Assumptions of Synthetic Control Methods

[6:45]
Differentially Private Densest Subgraph

(ends 7:00 AM)

7 a.m.

Oral
s
7:00-8:00

[7:00]
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits

[7:15]
Nonstochastic Bandits and Experts with Arm-Dependent Delays

[7:30]
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise

[7:45]
Towards an Understanding of Default Policies in Multitask Policy Optimization

(ends 8:00 AM)

9 a.m.

9:15 a.m.

Invited Talk:

Eric Tchetgen Tchetgen

(ends 10:15 AM)

10:15 a.m.

TUE 29 MAR

1 a.m.

(ends 2:30 AM)

2:30 a.m.

Oral
s
2:30-3:30

[2:30]
Adversarially Robust Kernel Smoothing

[2:45]
A Single-Timescale Method for Stochastic Bilevel Optimization

[3:00]
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization

[3:15]
Generative Models as Distributions of Functions

(ends 3:30 AM)

3:30 a.m.

Oral
s
3:30-4:30

[3:30]
Amortized Rejection Sampling in Universal Probabilistic Programming

[3:45]
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff

[4:00]
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function

[4:15]
On the Consistency of Max-Margin Losses

(ends 4:30 AM)

5:30 a.m.

Community Activities and Mentorship:

(ends 7:00 AM)

7 a.m.

Invited Talk:

Laure Zanna

(ends 8:00 AM)

8 a.m.

Oral
s
8:00-9:00

[8:00]
Many processors, little time: MCMC for partitions via optimal transport couplings

[8:15]
Rapid Convergence of Informed Importance Tempering

[8:30]
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models

[8:45]
Density Ratio Estimation via Infinitesimal Classification

(ends 9:00 AM)

9:45 a.m.

10 a.m.

Test Of Time:

(ends 11:00 AM)

11 a.m.

WED 30 MAR

midnight

Oral
s
12:00-1:00

[12:00]
Sampling from Arbitrary Functions via PSD Models

[12:15]
Orbital MCMC

[12:30]
Hardness of Learning a Single Neuron with Adversarial Label Noise

[12:45]
Data-splitting improves statistical performance in overparameterized regimes

(ends 1:00 AM)

1 a.m.

Oral
s
1:00-2:00

[1:00]
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

[1:15]
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning

[1:30]
A general class of surrogate functions for stable and efficient reinforcement learning

[1:45]
A Complete Characterisation of ReLU-Invariant Distributions

(ends 2:00 AM)

2:30 a.m.

Invited Talk:

Mihaela van der Schaar

(ends 3:30 AM)

3:30 a.m.

6 a.m.

Oral
s
6:00-7:00

[6:00]
Minimax Optimization: The Case of Convex-Submodular

[6:15]
Doubly Mixed-Effects Gaussian Process Regression

[6:30]
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes

[6:45]
Debiasing Samples from Online Learning Using Bootstrap

(ends 7:00 AM)

7 a.m.

Oral
s
7:00-8:00

[7:00]
Entropy Regularized Optimal Transport Independence Criterion

[7:15]
Two-Sample Test with Kernel Projected Wasserstein Distance

[7:30]
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression

[7:45]
Heavy-tailed Streaming Statistical Estimation

(ends 8:00 AM)

8:30 a.m.

(ends 10:00 AM)

10 a.m.