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Auditorium 1
Auditorium 1 Foyer

WED 1 MAY

10:30 p.m.

(ends 8:00 AM)

11:45 p.m.

THU 2 MAY

midnight

1 a.m.

Morning Coffee Break

1:30 a.m.

Orals 1:30-2:30

[1:30]
Conformal Contextual Robust Optimization

[1:30]
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games

[1:30]
Model-based Policy Optimization under Approximate Bayesian Inference

[1:30]
Online Learning of Decision Trees with Thompson Sampling

(ends 2:30 AM)

2:30 a.m.

3:30 a.m.

Lunch - retail

5 a.m.

Orals 5:00-6:15

[5:00]
The sample complexity of ERMs in stochastic convex optimization

[5:00]
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements

[5:00]
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses

[5:00]
Learning-Based Algorithms for Graph Searching Problems

[5:00]
Graph Partitioning with a Move Budget

(ends 6:15 AM)

6:15 a.m.

Afternoon Coffee Break

6:45 a.m.

Orals 6:45-8:00

[6:45]
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes

[6:45]
Reparameterized Variational Rejection Sampling

[6:45]
Intrinsic Gaussian Vector Fields on Manifolds

[6:45]
Generative Flow Networks as Entropy-Regularized RL

[6:45]
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics

(ends 8:00 AM)

8 a.m.

Posters 8:00-8:30

(ends 8:30 AM)

9 a.m.

10 p.m.

(ends 8:00 AM)

11 p.m.

FRI 3 MAY

midnight

1 a.m.

Morning Coffee Break

1:30 a.m.

Orals 1:30-2:30

[1:30]
Positivity-free Policy Learning with Observational Data

[1:30]
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits

[1:30]
Policy Learning for Localized Interventions from Observational Data

[1:30]
Exploration via linearly perturbed loss minimisation

(ends 2:30 AM)

2:30 a.m.

Orals 2:30-3:30

[2:30]
Membership Testing in Markov Equivalence Classes via Independence Queries

[2:30]
Causal Modeling with Stationary Diffusions

[2:30]
On the Misspecification of Linear Assumptions in Synthetic Controls

[2:30]
General Identifiability and Achievability for Causal Representation Learning

(ends 3:30 AM)

5 a.m.

6 a.m.

6:15 a.m.

Afternoon Coffee Break

7 a.m.

Orals 7:00-8:00

[7:00]
End-to-end Feature Selection Approach for Learning Skinny Trees

[7:00]
Probabilistic Modeling for Sequences of Sets in Continuous-Time

[7:00]
Learning to Defer to a Population: A Meta-Learning Approach

[7:00]
An Impossibility Theorem for Node Embedding

(ends 8:00 AM)

8 a.m.

10 p.m.

(ends 5:00 AM)

11 p.m.

SAT 4 MAY

midnight

Invited Talk:

Stefanie Jegelka

(ends 1:00 AM)

1 a.m.

Morning Coffee Break

1:30 a.m.

Orals 1:30-2:30

[1:30]
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors

[1:30]
Functional Flow Matching

[1:30]
Deep Classifier Mimicry without Data Access

[1:30]
Multi-Resolution Active Learning of Fourier Neural Operators

(ends 2:30 AM)

2:30 a.m.

Orals 2:30-3:30

[2:30]
Transductive conformal inference with adaptive scores

[2:30]
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers

[2:30]
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent

[2:30]
Testing exchangeability by pairwise betting

(ends 3:30 AM)

5 a.m.

Orals 5:00-6:00

[5:00]
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms

[5:00]
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry

[5:00]
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

[5:00]
Is this model reliable for everyone? Testing for strong calibration

(ends 6:00 AM)

6 a.m.

Posters 6:00-8:30

Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate

On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions

(ends 8:30 AM)