Search Results for author:
Found 3 papers, 2 papers with code
Function-space regularized Rényi divergences
We propose a new family of regularized R\'enyi divergences parametrized not only by the order $\alpha$ but also by a variational function space.
$(f,Γ)$-Divergences: Interpolating between $f$-Divergences and Integral Probability Metrics
We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both $f$-divergences and integral probability metrics (IPMs), such as the $1$-Wasserstein distance.
Variational Representations and Neural Network Estimation of Rényi Divergences
We further show that this R\'enyi variational formula holds over a range of function spaces; this leads to a formula for the optimizer under very weak assumptions and is also key in our development of a consistency theory for R\'enyi divergence estimators.
