ALOP Colloquium with Mathias Staudigl

On Monday, December 11, 2023 at 16:00 c.t. Prof. Dr. Mathias Staudigl, University of Mannheim, will speak at the ALOP Colloquium about his recent work:

A regularized variance-reduced modified extragradient method for stochastic hierarchical games

Abstract:

The theory of learning in games has so far focused mainly on games with simultaneous moves. Recently, researchers in machine learning have started investigating learning dynamics in games involving hierarchical decision-making. We consider an N-player hierarchical game in which the ith player’s objective comprises of an expectation-valued term, parametrized by rival decisions, and a hierarchical term. Such a framework allows for capturing a broad range of stochastic hierarchical optimization problems, Stackelberg equilibrium problems, and leader-follower games. We develop an iteratively regularized and smoothed variance-reduced modified extragradient framework for learning hierarchical equilibria in a stochastic setting. We equip our analysis with rate statements, complexity guarantees, and almost-sure convergence claims. We then extend these statements to settings where the lower-level problem is solved inexactly and provide the corresponding rate and complexity statements.

Please join us at 16:00 c.t. in HS 9.