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Sample Complexity of Sinkhorn divergences

Genevay, Aude; Chizat, Lenaic; Bach, Francis; Cuturi, Marco; Peyré, Gabriel (2019), Sample Complexity of Sinkhorn divergences, AISTATS'19 - 22nd International Conference on Artificial Intelligence and Statistics, 2019-04, Okinawa, Japan

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1810.02733.pdf (407.4Kb)
Type
Communication / Conférence
Date
2019
Titre du colloque
AISTATS'19 - 22nd International Conference on Artificial Intelligence and Statistics
Date du colloque
2019-04
Ville du colloque
Okinawa
Pays du colloque
Japan
Pages
11
Métadonnées
Afficher la notice complète
Auteur(s)
Genevay, Aude
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Chizat, Lenaic
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Bach, Francis
Département d'informatique - ENS Paris [DI-ENS]
Cuturi, Marco
Graduate School of Informatics [Kyoto]
Peyré, Gabriel
Département de Mathématiques et Applications - ENS Paris [DMA]
Résumé (EN)
Optimal transport (OT) and maximum mean discrepancies (MMD) are now routinely used in machine learning to compare probability measures. We focus in this paper on \emph{Sinkhorn divergences} (SDs), a regularized variant of OT distances which can interpolate, depending on the regularization strength ε, between OT (ε=0) and MMD (ε=∞). Although the tradeoff induced by that regularization is now well understood computationally (OT, SDs and MMD require respectively O(n3logn), O(n2) and n2 operations given a sample size n), much less is known in terms of their \emph{sample complexity}, namely the gap between these quantities, when evaluated using finite samples \emph{vs.} their respective densities. Indeed, while the sample complexity of OT and MMD stand at two extremes, 1/n1/d for OT in dimension d and 1/n−−√ for MMD, that for SDs has only been studied empirically. In this paper, we \emph{(i)} derive a bound on the approximation error made with SDs when approximating OT as a function of the regularizer ε, \emph{(ii)} prove that the optimizers of regularized OT are bounded in a Sobolev (RKHS) ball independent of the two measures and \emph{(iii)} provide the first sample complexity bound for SDs, obtained,by reformulating SDs as a maximization problem in a RKHS. We thus obtain a scaling in 1/n−−√ (as in MMD), with a constant that depends however on ε, making the bridge between OT and MMD complete.
Mots-clés
Sinkhorn divergences

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