The Jensen–Shannon divergence is a renowned bounded symmetrization of the
unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler …

A class of distance measures on probabilities--the integral probability metrics (IPMs)--is
addressed: these include the Wasserstein distance, Dudley metric, and Maximum Mean …

We study the centroid with respect to the class of information-theoretic Burbea-Rao
divergences that generalize the celebrated Jensen-Shannon divergence by measuring the …

Hermitian positive definite (hpd) matrices form a self-dual convex cone whose interior is a
Riemannian manifold of nonpositive curvature. The manifold view comes with a natural …

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by
considering a variational definition with respect to a generic mean, thereby extending the …

The Chernoff information between two probability measures is a statistical divergence
measuring their deviation defined as their maximally skewed Bhattacharyya distance …

We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual
complexes from the viewpoint of information geometry by considering the Fisher-Rao …

In this paper, we first give an introduction to the theoretical basis of the privacy-utility
equilibrium in federated learning based on Bayesian privacy definitions and total variation …

Markov Chain Monte Carlo methods for sampling from complex distributions and estimating
normalization constants often simulate samples from a sequence of intermediate …

A Jensen-Bregman divergence is a distortion measure defined by a Jensen convexity gap
induced by a strictly convex functional generator. Jensen-Bregman divergences unify the …