activity
20172020
most citedThe strictly-correlated electron functional for spherically symmetric systems revisited

19 citations · 52 across the 6 of their papers we have counts for

collaborators

8 papers

math.OC20209 cited

Optimal Transport losses and Sinkhorn algorithm with general convex regularization

Simone Di Marino, Augusto Gerolin

We introduce a new class of convex-regularized Optimal Transport losses, which generalizes the classical Entropy-regularization of Optimal Transport and Sinkhorn divergences, and p…

cs.LG20204 cited

Learning normalizing flows from Entropy-Kantorovich potentials

Chris Finlay, Augusto Gerolin, Adam M Oberman +1

We approach the problem of learning continuous normalizing flows from a dual perspective motivated by entropy-regularized optimal transport, in which continuous normalizing flows a…

stat.ML2020

Entropy-Regularized -Wasserstein Distance between Gaussian Measures

Anton Mallasto, Augusto Gerolin, Hà Quang Minh

Gaussian distributions are plentiful in applications dealing in uncertainty quantification and diffusivity. They furthermore stand as important special cases for frameworks providi…

physics.chem-ph20193 cited

Kinetic correlation functionals from the entropic regularisation of the strictly-correlated electrons problem

Augusto Gerolin, Juri Grossi, Paola Gori-Giorgi

We investigate whether the entropic regularisation of the strictly-correlated-electrons problem can be used to build approximations for the kinetic correlation energy functional at…

math.PR2019

An Optimal Transport approach for the Schrödinger bridge problem and convergence of Sinkhorn algorithm

Simone Di Marino, Augusto Gerolin

This paper exploit the equivalence between the Schrödinger Bridge problem and the entropy penalized optimal transport in order to find a different approach to the duality, in the s…

cs.LG201917 cited

How Well Do WGANs Estimate the Wasserstein Metric?

Anton Mallasto, Guido Montúfar, Augusto Gerolin

Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution. Recently, a popular choice for the similarity measure ha…