ISEM Seminar Series

AI “Agent Statistics”: AlphaGo for Global Statistical Optimization without Initial Value and Stepsize Constraints

by

Xiaodong Yan

Professor, School of Mathematics and Statistics

Xi'an Jiaotong University

26 December 2024 (Thursday), 5pm – 6pm
Venue: E1-07-21/22 - ISEM Executive Classroom
ABSTRACT

This work introduces a unified slot machine framework for global optimization, transforming the search for global optimizers into the formulation of an optimal bandit strategy over infinite policy sets. Inspired by AlphaGo's success with Monte Carlo Tree Search, we develop the Strategic Monte Carlo Optimization (SMCO) algorithm, which extends the exploration space by employing tree search methods. SMCO generates points coordinate-wise from paired distributions, facilitating parallel implementation for high-dimensional continuous functions. Unlike gradient descent ascent (GDA), which follows a single-directional path and depends on initial points and step sizes, SMCO takes a two-sided sampling approach, ensuring robustness to these parameters. We establish convergence to global optimizers almost surely and prove a strategic law of large numbers for nonlinear expectations. Numerical results demonstrate that SMCO outperforms GDA, particle swarm optimization, and simulated annealing in both speed and accuracy.

Dr Xiaodong Yan is a Professor at the School of Mathematics and Statistics, Xi'an Jiaotong University. He has been selected into the national-level young talent project and the A support plan for outstanding young scholars of Xi'an Jiaotong University. He is also a CCF-Didi Gaia Scholar. His research direction is statistical machine learning and Agent statistics, and his academic papers have been published in well-known statistical journals such as JRSSB, AOS, JASA, the famous econometrics journal JOE, as well as top artificial intelligence conferences like AAAI.