1 citations · 1 across the 1 of their papers we have counts for
3 papers
stat.ML2023
Learning Elastic Costs to Shape Monge Displacements
Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin +3
Given a source and a target probability measure supported on , the Monge problem asks to find the most efficient way to map one distribution to the other. This effici…
cs.LG2023★ 1 cited
Unbalanced Low-rank Optimal Transport Solvers
Meyer Scetbon, Michal Klein, Giovanni Palla +1
The relevance of optimal transport methods to machine learning has long been hindered by two salient limitations. First, the computational cost of standard sample-based so…
stat.ML2023★ 1 cited
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Marco Cuturi, Michal Klein, Pierre Ablin
Optimal transport (OT) theory focuses, among all maps that can morph a probability measure onto another, on those that are the ``thriftiest…