442 citations · 2.5k across the 75 of their papers we have counts for
77 papers
Simulation-based Inference for Cardiovascular Models
Antoine Wehenkel, Laura Manduchi, Jens Behrmann +6
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely u…
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…
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…
The Monge Gap: A Regularizer to Learn All Transport Maps
Théo Uscidda, Marco Cuturi
Optimal transport (OT) theory has been been used in machine learning to study and characterize maps that can push-forward efficiently a probability measure onto another. Recent wor…
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…
Supervised Training of Conditional Monge Maps
Charlotte Bunne, Andreas Krause, Marco Cuturi
Optimal transport (OT) theory describes general principles to define and select, among many possible choices, the most efficient way to map a probability measure onto another. That…