Strongly correlated particle systems: a toolbox for machine intelligence

Abstract

The classical paradigm of randomness in the sciences is that of i.i.d. random variables, and going beyond i.i.d. is often considered a difficulty and a challenge to be overcome. In this talk, we will explore a new perspective, wherein strongly constrained random systems in fact help to understand fundamental problems in machine learning. In particular, we will discuss strongly correlated particle systems  that are well-motivated from statistical and quantum physics, including in particular determinantal probability measures. These will be used to shed important light on questions of fundamental interest in learning theory, focussing on applications to novel sampling techniques and advances in stochastic gradient descent.