InfoBridge: Mutual Information estimation via Bridge Matching

Abstract

Diffusion bridge models have recently become a powerful tool in the field of generative modeling. In this work, we leverage their power to address another important problem in machine learning and information theory - the estimation of the mutual information (MI) between two random variables. We show that by using the theory of diffusion bridges, one can construct an unbiased estimator for data posing difficulties for conventional MI estimators. We showcase the performance of our estimator on a series of standard MI estimation benchmarks.

Alexander Korotin

Assistant professor,
senior research scientist

My research interests include generative modeling, unpaired learning, optimal transport and Schrodinger bridges.