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math.OC2024
Mirror and Preconditioned Gradient Descent in Wasserstein Space
Clément Bonet, Théo Uscidda, Adam David +2
As the problem of minimizing functionals on the Wasserstein space encompasses many applications in machine learning, different optimization algorithms on have receiv…
math.OC2022★ 1 cited
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
Pierre-Cyril Aubin-Frankowski, Anna Korba, Flavien Léger
Many problems in machine learning can be formulated as optimizing a convex functional over a vector space of measures. This paper studies the convergence of the mirror descent algo…
math.OC2020★ 2 cited
The Wasserstein Proximal Gradient Algorithm
Adil Salim, Anna Korba, Giulia Luise
Wasserstein gradient flows are continuous time dynamics that define curves of steepest descent to minimize an objective function over the space of probability measures (i.e., the W…