[ML Seminar] Machine Learning Seminar, 11/11 at 13:30: Isabel Haasler, "An introduction to multi-marginal optimal transport – with applications to control theory (and machine learning)"

[Louis Ohl]

Dear colleagues,

A reminder that the ML seminar is in 10 minutes in John von Neumann,

Best,
Louis

Skickat från Outlook för Android<https://aka.ms/AAb9ysg>
________________________________
From: Louis Ohl <louis.ohl at liu.se>
Sent: Tuesday, October 28, 2025 9:25:28 AM
To: ml-seminars at lists.liu.se <ml-seminars at lists.liu.se>; idaint at ida.liu.se <idaint at ida.liu.se>
Subject: Machine Learning Seminar, 11/11 at 13:30: Isabel Haasler, "An introduction to multi-marginal optimal transport  – with applications to control theory (and machine learning)"

Welcome to an IDA Machine Learning Seminar on Tuesday, November 11 at 13:30 In John Von Neumann

An introduction to multi-marginal optimal transport – with applications to control theory (and machine learning)

Isabel Haasler<https://sites.google.com/view/isabelhaasler/home>, Assistant professor at University of Uppsala

Abstract Optimal transport is a mathematical framework originally developed to study the most cost-efficient way to move mass from one distribution to another. The Schrödinger bridge problem is a stochastic version of this problem, which is essentially a maximum likelihood estimation problem between two distributions. In recent years, optimal transport and Schrödinger bridges have found powerful new applications in machine learning and control theory. In this talk I will introduce an extension of the problem to multiple distribution. We will see how such multi-marginal problems with additional structures appear in control and estimation problems of large populations of agents. Moreover, I will point to several connections to machine learning applications, in particular generative models.

Location: John Von Neumann, https://link.mazemap.com/jPVeR5ZQ

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--
Louis Ohl
Postdoc
louis.ohl at liu.se

Linköping University
IDA / STIMA
B-Huset, 2A:445
Webpage<https://oshillou.github.io/>
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