Seminar by Justin Ko

Monday, October 2, 2023 11:30 am - 12:30 pm EDT

Probability seminar series 

Justin Ko
University of Waterloo

Room: M3 3127

Estimating Rank-One Matrices with Mismatched Prior and Noise: Universality and Large Deviations

We study a generic class of Bayesian inference models with a mismatch in the prior and noise. We will explain a universality result that reduces the mutual information for inference problems in the mismatched setting to the computation of a modified spin glass free energy. We prove an almost sure large deviations principle for the overlaps between the truth and samples from the posterior. As a consequence, we recover the limit of the mutual information in mismatched inference problems. This is joint work with Alice Guionnet, Florent Krzakala, and Lenka Zdeborova.