CORE MACHINE LEARNING

Meta Optimal Transport

July 20, 2023

Abstract

We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to rapidly predict and solve new problems. Otherwise, standard methods ignore the knowledge of the past solutions and suboptimally re-solve each problem from scratch. We instantiate Meta OT models in discrete and continuous settings between grayscale images, spherical data, classification labels, and color palettes and use them to improve the computational time of standard OT solvers.

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AUTHORS

Written by

Brandon Amos

Giulia Luise

Ievgen Redko

Samuel Cohen

Publisher

ICML

Research Topics

Core Machine Learning

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