Optimal Transport for Machine Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated April 2026

Optimal transport provides geometry-aware distances between distributions. Theory, computation, and applications to ML.

1. Optimal Transport: Old and New
   Villani. Grundlehren der mathematischen Wissenschaften, Springer 2008.
2. Computational Optimal Transport
   Peyre and Cuturi. Foundations and Trends in ML 2019.
3. Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
   Cuturi. NeurIPS 2013.
4. Wasserstein GAN
   Arjovsky, Chintala, Bottou. ICML 2017.
5. Sliced Wasserstein Distance for Learning Gaussian Mixture Models
   Kolouri et al.. CVPR 2018.
6. Data-driven Distributionally Robust Optimization Using the Wasserstein Metric
   Mohajerin Esfahani and Kuhn. Mathematical Programming 2018.
7. Optimal Transport for Domain Adaptation
   Courty et al.. TPAMI 2017.
8. Approximating 1-Wasserstein Distance with Trees
   Yamada et al.. TMLR 2022.
9. Distributional Reinforcement Learning with Quantile Regression
   Dabney et al.. AAAI 2018.