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TUE 25 APR
7 a.m.
(ends 5:00 PM)
8:45 a.m.
Opening Remarks:
(ends 9:00 AM)
9 a.m.
Invited Talk:
Arthur Gretton
(ends 10:00 AM)
10 a.m.
Coffee Break
10:30 a.m.
Orals 10:30-11:30
[10:30] The Schrödinger Bridge between Gaussian Measures has a Closed Form
[10:45] Rethinking Initialization of the Sinkhorn Algorithm
[11:00] Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks
[11:15] Mode-Seeking Divergences: Theory and Applications to GANs
(ends 11:30 AM)
11:30 a.m.
Affinity Panel:
(ends 12:30 PM)
12:30 p.m.
Lunch Break
3 p.m.
Coffee Break
3:30 p.m.
Orals 3:30-4:30
[3:30] The Power of Recursion in Graph Neural Networks for Counting Substructures
[3:45] Implicit Graphon Neural Representation
[4:00] Implications of sparsity and high triangle density for graph representation learning
[4:15] Fitting low-rank models on egocentrically sampled partial networks
(ends 4:30 PM)
4:30 p.m.
Posters 4:30-7:00
(ends 7:00 PM)
6 p.m.
Affinity Event:
(ends 8:00 PM)
WED 26 APR
7 a.m.
(ends 5:00 PM)
10 a.m.
Coffee Break
10:30 a.m.
Orals 10:30-11:30
[10:30] Do Bayesian Neural Networks Need To Be Fully Stochastic?
[10:45] Indeterminacy in Generative Models: Characterization and Strong Identifiability
[11:00] Distance-to-Set Priors and Constrained Bayesian Inference
[11:15] Particle algorithms for maximum likelihood training of latent variable models
(ends 11:30 AM)
11:30 a.m.
Orals 11:30-12:30
[11:30] BaCaDI: Bayesian Causal Discovery with Unknown Interventions
[11:45] Multilevel Bayesian Quadrature
[12:00] Discovering Many Diverse Solutions with Bayesian Optimization
[12:15] Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
(ends 12:30 PM)
12:30 p.m.
Lunch Break
2 p.m.
Test Of Time Award:
(ends 3:00 PM)
3 p.m.
Coffee Break
3:30 p.m.
Orals 3:30-4:30
[3:30] Huber-robust confidence sequences
[3:45] Error Estimation for Random Fourier Features
[4:00] A Tale of Sampling and Estimation in Discounted Reinforcement Learning
[4:15] Safe Sequential Testing and Effect Estimation in Stratified Count Data
(ends 4:30 PM)
4:30 p.m.
Posters 4:30-7:00
(ends 7:00 PM)
THU 27 APR
7 a.m.
(ends 12:00 PM)
8 a.m.
Mentorship:
(ends 9:00 AM)
10 a.m.
Coffee Break
10:30 a.m.
Orals 10:30-11:30
[10:30] Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
[10:45] Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
[11:00] Blessing of Class Diversity in Pre-training
[11:15] Federated Learning under Distributed Concept Drift
(ends 11:30 AM)
11:30 a.m.
Orals 11:30-12:30
[11:30] Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover
[11:45] Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate
[12:00] An Efficient and Continuous Voronoi Density Estimator
[12:15] Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
(ends 12:30 PM)
12:30 p.m.
Lunch Break
2 p.m.
Posters 2:00-4:30
(ends 4:30 PM)