no code implementations • 15 Feb 2018 • Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada
Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention.
no code implementations • ICLR 2018 • Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions.
2 code implementations • 7 Nov 2017 • Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT plan and Monge map between the underlying continuous measures.
1 code implementation • 17 Oct 2017 • Mathieu Blondel, Vivien Seguy, Antoine Rolet
In this paper, we explore regularizing the primal and dual OT formulations with a strongly convex term, which corresponds to relaxing the dual and primal constraints with smooth approximations.