[ Logo] Artificial Intelligence and Statistics 2023

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Program Schedule

Schedule is tentative and is subject to changes!

All times are CEST. You can check current CEST time here.

Registration Desk

Registration desk is open on:

Schedule for Day 1: Tue, April 25

Time (CEST) Day 1: Tue, April 25
08:45-09:00 Opening remarks
09:00-10:00 Keynote Talk: Arthur Gretton (UCL Gatsby)
10:00-10:30 Coffee break
10:30-11:30
Oral Session 1 | Optimal Transport, Information Theory
  • The Schrödinger Bridge between Gaussian Measures has a Closed Form
  • Rethinking Initialization of the Sinkhorn Algorithm
  • Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks
  • Mode-Seeking Divergences: Theory and Applications to GANs
11:30-12:30 Affinity Groups Panel
12:30-14:00 Lunch break
14:00-15:00
Oral Session 2 | Trustworthy ML and Statistics
  • Who Should Predict? Exact Algorithms For Learning to Defer to Humans
  • Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
  • Origins of Low-Dimensional Adversarial Perturbations
  • Data Banzhaf: A Robust Data Valuation Framework for Machine Learning
15:00-15:30 Coffee break
15:30-16:30
Oral Session 3 | Representations of Graphs
  • The Power of Recursion in Graph Neural Networks for Counting Substructures
  • Implicit Graphon Neural Representation
  • Implications of sparsity and high triangle density for graph representation learning
  • Fitting low-rank models on egocentrically sampled partial networks
16:30-19:00 Poster session 1

Schedule for Day 2: Wed, April 26th

Time (CEST) Day 2: Wed, April 26th
08:00-09:00 Mentoring Event 1
09:00-10:00 Keynote Talk: Shakir Mohamed (Deepmind)
10:00-10:30 Coffee break
10:30-11:30
Oral Session 4 | Probabilistic Methods 1
  • Do Bayesian Neural Networks Need To Be Fully Stochastic?
  • Indeterminacy in Generative Models: Characterization and Strong Identifiability
  • Distance-to-Set Priors and Constrained Bayesian Inference
  • Particle algorithms for maximum likelihood training of latent variable models
11:30-12:30
Oral Session 5 | Probabilistic Methods 2
  • BaCaDI: Bayesian Causal Discovery with Unknown Interventions
  • Multilevel Bayesian Quadrature
  • Discovering Many Diverse Solutions with Bayesian Optimization
  • Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
12:30-14:00 Lunch break
14:00-15:00 Test of Time Award: Andreas Damianou and Neil Lawrence
Deep Gaussian Processes (published at AISTATS 2013)
15:00-15:30 Coffee break
15:30-16:30
Oral Session 6 | Statistical Methods 1
  • Huber-robust confidence sequences
  • Error Estimation for Random Fourier Features
  • A Tale of Sampling and Estimation in Discounted Reinforcement Learning
  • Safe Sequential Testing and Effect Estimation in Stratified Count Data
16:30-19:00 Poster session 2

Schedule for Day 3: Thu, April 27th

Time (CEST) Day 2: Wed, April 26th
08:00-09:00 Mentoring Event 2
09:00-10:00 Keynote Talk: Tamara Broderick (MIT)
10:00-10:30 Coffee break
10:30-11:30
Oral Session 7 | Supervised Learning
  • Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
  • Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
  • Blessing of Class Diversity in Pre-training
  • Federated Learning under Distributed Concept Drift
11:30-12:30
Oral Session 8 | Statistical Methods 2
  • Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover
  • Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate
  • An Efficient and Continuous Voronoi Density Estimator
  • Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
12:30-14:00 Lunch break
14:00-16:30 Poster session 3
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