[2505.06589v1] Optimal Transport for Machine Learners

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack

[2505.06589v1] Optimal Transport for Machine Learners Skip to main content Search arXiv Press Enter to search · Advanced search --> Statistics > Machine Learning arXiv:2505.06589v1 (stat) [Submitted on 10 May 2025 (this version), latest version 14 Jun 2026 ( v2 )] Title: Optimal Transport for Machine Learners Authors: Gabriel Peyré View a PDF of the paper titled Optimal Transport for Machine Learners, by Gabriel Peyr\'e View PDF HTML (experimental) Abstract: Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probabilit

related reading

• Quick start guide - POT Python Optimal Transport 0.9.5 documentationpythonot.github.io
• Your Transformer is Secretly an EOT Solver | Elements of a Vector Spaceelonlit.com
• Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
• The Little Book of Deep Learningfleuret.org
• GenAI Handbookgenai-handbook.github.io
• [2212.07677] Transformers learn in-context by gradient descentarxiv.org
• LaCoOT: Layer Collapse through Optimal Transportarxiv.org
• Wasserstein metric - Wikipediaen.wikipedia.org
• Transformer Math 101 | EleutherAI Blogblog.eleuther.ai
• microgptkarpathy.github.io
• The Principles of Diffusion Modelsarxiv.org
• The Principles of Diffusion Modelsarxiv.org