38 citations · 57 across the 7 of their papers we have counts for
Showing 2020 · math.OCShow all
2 papers · 2 filters
math.OC2020
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré
Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between su…
math.OC2020
Online Sinkhorn: Optimal Transport distances from sample streams
Arthur Mensch, Gabriel Peyré
Optimal Transport (OT) distances are now routinely used as loss functions in ML tasks. Yet, computing OT distances between arbitrary (i.e. not necessarily discrete) probability dis…