38 citations · 57 across the 7 of their papers we have counts for
4 papers · 1 filter
Distribution-Based Invariant Deep Networks for Learning Meta-Features
Gwendoline De Bie, Herilalaina Rakotoarison, Gabriel Peyré +1
Recent advances in deep learning from probability distributions successfully achieve classification or regression from distribution samples, thus invariant under permutation of the…
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
Hicham Janati, Boris Muzellec, Gabriel Peyré +1
Although optimal transport (OT) problems admit closed form solutions in a very few notable cases, e.g. in 1D or between Gaussians, these closed forms have proved extremely fecund f…
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…
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, Thomas Moreau
In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approxima…