Programs

Organizers

Eric Moulines, MBZUAI, Ecole Polytechnique

Alexey Naumov, HSE University, Steklov Mathematical Institute of RAS

Sergey Samsonov, HSE University

Yuhao Wang (王禹皓), Tsinghua University

Abstract

The Statistical AI workshop focuses on the statistical ideas that make modern AI reliable - how to quantify uncertainty, make decisions under uncertainty, and design theoretically grounded algorithms that still scale to large data settings. As models grow larger and are deployed more widely, these questions become both more urgent and more technically challenging. The workshop will bring together researchers across statistics, machine learning, and applied AI to discuss recent results and open problems. The workshop will cover, but is not limited to, the following key topics:

● Statistical decision making

● Generative modelling and sampling

● Conformal prediction

● Statistical and causal inference

● Stochastic optimisation, stochastic approximation, and optimization methods for large-scale and non-convex AI models