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MON 24 APR
10 p.m.
(ends 8:00 AM)
11:45 p.m.
Opening Remarks:
(ends 12:00 AM)
TUE 25 APR
midnight
Invited Talk:
Arthur Gretton
(ends 1:00 AM)
1 a.m.
Coffee Break
1:30 a.m.
Orals 1:30-2:30
[1:30] The Schrödinger Bridge between Gaussian Measures has a Closed Form
[1:45] Rethinking Initialization of the Sinkhorn Algorithm
[2:00] Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks
[2:15] Mode-Seeking Divergences: Theory and Applications to GANs
(ends 2:30 AM)
2:30 a.m.
Affinity Panel:
(ends 3:30 AM)
3:30 a.m.
Lunch Break
6 a.m.
Coffee Break
6:30 a.m.
Orals 6:30-7:30
[6:30] The Power of Recursion in Graph Neural Networks for Counting Substructures
[6:45] Implicit Graphon Neural Representation
[7:00] Implications of sparsity and high triangle density for graph representation learning
[7:15] Fitting low-rank models on egocentrically sampled partial networks
(ends 7:30 AM)
7:30 a.m.
Posters 7:30-10:00
(ends 10:00 AM)
9 a.m.
Affinity Event:
(ends 11:00 AM)
10 p.m.
(ends 8:00 AM)
WED 26 APR
1 a.m.
Coffee Break
1:30 a.m.
Orals 1:30-2:30
[1:30] Do Bayesian Neural Networks Need To Be Fully Stochastic?
[1:45] Indeterminacy in Generative Models: Characterization and Strong Identifiability
[2:00] Distance-to-Set Priors and Constrained Bayesian Inference
[2:15] Particle algorithms for maximum likelihood training of latent variable models
(ends 2:30 AM)
2:30 a.m.
Orals 2:30-3:30
[2:30] BaCaDI: Bayesian Causal Discovery with Unknown Interventions
[2:45] Multilevel Bayesian Quadrature
[3:00] Discovering Many Diverse Solutions with Bayesian Optimization
[3:15] Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
(ends 3:30 AM)
3:30 a.m.
Lunch Break
5 a.m.
Test Of Time Award:
(ends 6:00 AM)
6 a.m.
Coffee Break
6:30 a.m.
Orals 6:30-7:30
[6:30] Huber-robust confidence sequences
[6:45] Error Estimation for Random Fourier Features
[7:00] A Tale of Sampling and Estimation in Discounted Reinforcement Learning
[7:15] Safe Sequential Testing and Effect Estimation in Stratified Count Data
(ends 7:30 AM)
7:30 a.m.
Posters 7:30-10:00
(ends 10:00 AM)
10 p.m.
(ends 3:00 AM)
11 p.m.
Mentorship:
(ends 12:00 AM)
THU 27 APR
1 a.m.
Coffee Break
1:30 a.m.
Orals 1:30-2:30
[1:30] Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
[1:45] Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
[2:00] Blessing of Class Diversity in Pre-training
[2:15] Federated Learning under Distributed Concept Drift
(ends 2:30 AM)
2:30 a.m.
Orals 2:30-3:30
[2:30] Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover
[2:45] Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate
[3:00] An Efficient and Continuous Voronoi Density Estimator
[3:15] Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
(ends 3:30 AM)
3:30 a.m.
Lunch Break
5 a.m.
Posters 5:00-7:30
(ends 7:30 AM)