Carissimi colleghi, scusandomi per eventuali invii multipli, vi inoltro il seguente annuncio di seminario. Tutti gli interessati sono invitati a partecipare. Cordialmente, Enea Bongiorno -------------------------------- - data e orario: 9 Aprile 2024 ore 14.30 - sala riunioni primo piano, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale. - on-line: meet.google.com/isi-ipsn-tpa *Title:* From Monge to Huber through a Schrödinger Bridge: Robust Optimal Transport in Large Dimensions *Prof. Davide La Vecchia*, University of Geneva *Abstract*: Optimal Transport (OT) plays an increasingly important role in modern statistics and machine learning. I will begin the talk with a gentle introduction to the OT problem, first in its classical Monge formulation andthen in the relaxed Kantorovich framework, highlighting their statistical relevance and limitations. I will then present two recent advances that address the sensitivity of classical OT to outliers and heavy-tailed distributions. The first is the Robust Optimal Transport (ROBOT) framework, which leads to the robust Wasserstein Distance , a Huberised version of . I discuss its main properties,concentration inequalities, and consequences for minimum-distance estimation,together with applications in statistics (e.g., location estimation under heavy tails) and in machine learning (e.g., Generative Adversarial Networks). I will illustrate that ROBOT is effective in handling outlying values but is still sensitive to the dimensionality of the data. Thus, in the second part of the talk I introduce E-ROBOT, which integrates ROBOT with entropic regularization through connections to the Schrödinger bridge problem. This yields the robust Sinkhorn divergence , which enjoys dimension-free sample complexity of order, where n denotes the sample size. I briefly illustrate its use in large dimensional tasks in statistics, image analysis, and machine learning (e.g., goodness-of-fit testing, barycenter computation for corrupted shapes, gradient flows, and image colour transfer). Overall, the talk provides a unified perspective on robustness and regularization in OT, together with theoretical guarantees and practical computational tools. The talk is based on: Inference via robust optimal transportation: theory and methods, Y. Ma, H. Liu, D. La Vecchia & M. Lerasle, International Statistical Review, Available online from 26 June 2025, https://doi.org/10.1111/insr.70000 E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge, D. La Vecchia & H.Liu, 2025, https://arxiv.org/pdf/2509.11532 ----------------------------------- Locandina dell'evento <https://drive.google.com/file/d/1LKDh1zL716hVIJckknL0gxf-N8rYtz6u/view?usp=sharing> Seminari Matematici Statistici <https://sites.google.com/uniupo.it/seminari-ms/home-page> ----------------------------------- -- Enea G. Bongiorno, Università degli Studi del Piemonte Orientale - Amedeo Avogadro Via Perrone 18, 28100, Novara, Italia Phone: +390321375317 enea.bongiorno@uniupo.it upobook.uniupo.it/enea.bongiorno ------ Math-Stat Seminars at UPO seminari-ms.uniupo.it/home-page ------