Optimal transport aligns samples across distributions by minimizing the transportation cost
between them, eg, the geometric distances. Yet, it ignores coherence structure in the data …

We study distributionally robust optimization (DRO) with Sinkhorn distance--a variant of
Wasserstein distance based on entropic regularization. We derive convex programming …

In this paper, we propose a new framework named Communication Optimal Transport
(CommOT) for computing the rate distortion (RD) function. This work is motivated by …

In this monograph, we develop a mathematical framework based on asymptotically good
random structured codes, ie, codes possessing algebraic properties, for network information …

We study an extension of lossy compression where the reconstruction distribution is different
from the source distribution in order to account for distributional shift due to processing. We …

We study an extension of lossy compression where the reconstruction is subject to a
distribution constraint which can be different from the source distribution. We formulate our …

The distance that compares the difference between two probability distributions plays a
fundamental role in statistics and machine learning. Optimal transport (OT) theory provides a …

We develop a new upper bound on the capacity of the relay channel that is tighter than
previously known upper bounds. This upper bound is proved using traditional weak …

We consider the rate-limited quantum-to-classical optimal transport in terms of output-
constrained rate-distortion coding for discrete quantum measurement systems with limited …

Information geometry is a study of statistical manifolds, that is, spaces of probability
distributions from a geometric perspective. Its classical information-theoretic applications …