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TUE 25 APR
7 a.m.
(ends 5:00 PM)
8:45 a.m.
9 a.m.
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.
12:30 p.m.
Lunch Break
2 p.m.
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.
WED 26 APR
7 a.m.
(ends 5:00 PM)
9 a.m.
Invited Talk:
Shakir Mohamed
(ends 10:00 AM)
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.
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.
9 a.m.
Invited Talk:
Tamara Broderick
(ends 10:00 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)