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The Ethereal
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
November 18, 2022 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Marin Ballu, Quentin Berthet
arXiv ID
2211.10420
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
8
Venue
International Conference on Machine Learning
Last Checked
4 months ago
Abstract
Optimal transport is an important tool in machine learning, allowing to capture geometric properties of the data through a linear program on transport polytopes. We present a single-loop optimization algorithm for minimizing general convex objectives on these domains, utilizing the principles of Sinkhorn matrix scaling and mirror descent. The proposed algorithm is robust to noise, and can be used in an online setting. We provide theoretical guarantees for convex objectives and experimental results showcasing it effectiveness on both synthetic and real-world data.
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