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cs.LG2025
Learning Brenier Potentials with Convex Generative Adversarial Neural Networks
Claudia Drygala, Hanno Gottschalk, Thomas Kruse +2
Brenier proved that under certain conditions on a source and a target probability measure there exists a strictly convex function such that its gradient is a transport map from the…
cs.LG2025
Variable Bregman Majorization-Minimization Algorithm and its Application to Dirichlet Maximum Likelihood Estimation
Ségolène Martin, Jean-Christophe Pesquet, Gabriele Steidl +1
We propose a novel Bregman descent algorithm for minimizing a convex function that is expressed as the sum of a differentiable part (defined over an open set) and a possibly nonsmo…
cs.LG2025
Deep Generative Clustering with VAEs and Expectation-Maximization
Michael Adipoetra, Ségolène Martin
We propose a novel deep clustering method that integrates Variational Autoencoders (VAEs) into the Expectation-Maximization (EM) framework. Our approach models the probability dist…