Uploaded May 2025 | Updated September 2026, 1 week ago
In this video, Marco Cuturi from Apple, France, provides an in-depth overview of optimal transport (OT) computations, with a focus on the challenge of computing OT maps using two samples from high-dimensional probability measures. After reviewing several popular methods for this task, including those that utilize neural architectures, Cuturi introduces his recent work on parameterizing OT problems with elastic costs. These costs combine classic squared Euclidean distance with regularizers like the L1 norm. He’ll explain the properties of OT maps that follow such costs, and present new methods for both computing ground truth OT maps with elastic costs and adaptively learning the parameters of the regularizer.
This talk took place on Thursday, October 24 at the 2024 SIAM Conference on Mathematics of Data Science.
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Join SIAM siam.org/membership/individual-membership
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#apple #optimization #transportation #probability #neuralnetworks #datascience #dataanalytics #euclidean #machinelearning #mathematics #mathematicseducation
In this video, Marco Cuturi from Apple, France, provides an in-depth overview of optimal transport (OT) computations, with a focus on the challenge of computing OT maps using two samples from high-dimensional probability measures. After reviewing several popular methods for this task, including those that utilize neural architectures, Cuturi introduces his recent work on parameterizing OT problems with elastic costs. These costs combine classic squared Euclidean distance with regularizers like the L1 norm. He’ll explain the properties of OT maps that follow such costs, and present new methods for both computing ground truth OT maps with elastic costs and adaptively learning the parameters of the regularizer.
This talk took place on Thursday, October 24 at the 2024 SIAM Conference on Mathematics of Data Science.
Learn more about SIAM siam.org
Join SIAM siam.org/membership/individual-membership
Attend a SIAM Conference siam.org/conferences-events
#apple #optimization #transportation #probability #neuralnetworks #datascience #dataanalytics #euclidean #machinelearning #mathematics #mathematicseducation










