[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2022


AISTATS 2022 Oral Sessions Schedule

All times are UTC. You can check current UTC time here and convert to other time zones using online time zones converters such as utctime.net or time.is. Be aware of the summer time change in Europe on March 27.

Day 1: Mon, March 28

(click on session titles to show the list of papers)

Session Title Time (UTC)
Oral Session 1 | Learning theory / General ML
  • Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
  • Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
  • Survival regression with proper scoring rules and monotonic neural networks
  • Multivariate Quantile Function Forecaster
08:30 - 09:30
Oral Session 2 | Bayesian methods / Sampling methods
  • Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
  • Nonparametric Relational Models with Superrectangulation
  • Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
  • Unifying Importance Based Regularisation Methods for Continual Learning
09:30 - 10:30
Oral Session 3 | Causality / Trustworthy ML
  • Almost optimal universal lower bound for learning causal DAGs with atomic interventions
  • Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
  • On the Assumptions of Synthetic Control Methods
  • Differentially Private Densest Subgraph
13:00 - 14:00
Oral Session 4 | Bandits / Reinforcement learning
  • Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
  • Nonstochastic Bandits and Experts with Arm-Dependent Delays
  • Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
  • Towards an Understanding of Default Policies in Multitask Policy Optimization
14:00 - 15:00

Day 2: Tue, March 29

(click on session titles to show the list of papers)

Session Title Time (UTC)
Oral Session 5 | Kernels / Optimization / Deep learning
  • Kernel Robust Smoothing
  • A Single-Timescale Method for Stochastic Bilevel Optimization
  • Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
  • Generative Models as Distributions of Functions
09:30 - 10:30
Oral Session 6 | Learning theory / Sampling methods
  • Amortized Rejection Sampling in Universal Probabilistic Programming
  • Adaptive Importance Sampling meets Mirror Descent : a Bias-variance tradeoff
  • "Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose your Model, not your Loss Function"
  • On the Consistency of Max-Margin Losses
10:30 - 11:30
Oral Session 7 | Bayesian methods / Deep learning
  • "Many processors, little time: MCMC for partitions via optimal transport couplings"
  • Rapid Convergence of Informed Importance Tempering
  • Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
  • Density Ratio Estimation via Infinitesimal Classification
15:00 - 16:00

Day 3: Wed, March 30

(click on session titles to show the list of papers)

Session Title Time (UTC)
Oral Session 8 | Learning theory / Sampling methods
  • Sampling from Arbitrary Functions via PSD Models
  • Orbital MCMC
  • Hardness of Learning a Single Neuron with Adversarial Label Noise
  • Data-splitting improves statistical performance in overparameterized regimes
07:00 - 08:00
Oral Session 9 | Reinforcement learning / Deep learning
  • Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
  • "Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Gradient Temporal-Difference Learning"
  • A general class of surrogate functions for stable and efficient reinforcement learning
  • A Complete Characterisation of ReLU-Invariant Distributions
08:00 - 09:00
Oral Session 10 | Gaussian processes / Optimization / Online ML
  • Minimax Optimization: The Case of Convex-Submodular
  • Doubly Mixed-Effects Gaussian Process Regression
  • Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
  • Debiasing Samples from Online Learning Using Bootstrap
13:00 - 14:00
Oral Session 11 | Bayesian methods / Sampling methods
  • Entropy Regularized Optimal Transport Independence Criterion
  • Two-Sample Test with Kernel Projected Wasserstein Distance
  • Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
  • Heavy-tailed Streaming Statistical Estimation
14:00 - 15:00
This site last compiled Sun, 05 Feb 2023 13:35:30 +0100
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