[ Logo] Artificial Intelligence and Statistics 2023


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
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
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
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
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
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
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
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
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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