mlsanityData to 7 Oct 2026

Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

arXiv 2201.12324 ↗First posted 28 Jan 2022cs.LG stat.MLcited by 96

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Abstract (arXiv)

Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms. The toolbox builds on various JAX features, such as automatic and custom reverse mode differentiation, vectorization, just-in-time compilation and accelerators support. The toolbox covers elementary computations, such as the resolution of the regularized OT problem, and more advanced extensions, such as barycenters, Gromov-Wasserstein, low-rank solvers, estimation of convex maps, differentiable generalizations of quantiles and ranks, and approximate OT between Gaussian mixtures. The toolbox code is available at texttthttps://github.com/ott-jax/ott

Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

8 of 17 references.

References · 8

  1. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  2. 2016Input Convex Neural Networkscited by 407
  3. 2019Differentiable Ranks and Sorting using Optimal Transportcited by 113
  4. 2019Statistical bounds for entropic optimal transport: sample complexity and the central limit theoremcited by 85
  5. 2019A Wasserstein-type distance in the space of Gaussian Mixture Modelscited by 75
  6. 2021Low-Rank Sinkhorn Factorizationcited by 66
  7. 2021Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costscited by 61
  8. 2018Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distancecited by 56

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 21; 2024: 31; 2025: 21; 2026: 10.

96 papers.

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  1. 2022Soft Diffusion: Score Matching for General Corruptionscited by 101
  2. 2022Action Matching: Learning Stochastic Dynamics from Samplescited by 94
  3. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  4. 2022Supervised Training of Conditional Monge Mapscited by 60
  5. 2024Sequential Controlled Langevin Diffusionscited by 47
  6. 2023Optimal Transport for Offline Imitation Learningcited by 42
  7. 2023Aligned Diffusion Schr"odinger Bridgescited by 38
  8. 2024Simple ReFlow: Improved Techniques for Fast Flow Modelscited by 34
  9. 2023Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Modelscited by 33
  10. 2023The Monge Gap: A Regularizer to Learn All Transport Mapscited by 32
  11. 2023Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translationcited by 32
  12. 2024Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMscited by 32
38 more
  1. 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemscited by 29
  2. 2022Meta Optimal Transportcited by 28
  3. 2025Underdamped Diffusion Bridges with Applications to Samplingcited by 28
  4. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  5. 2023GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomicscited by 27
  6. 2024Wasserstein Flow Matching: Generative modeling over families of distributionscited by 27
  7. 2024Addressing Misspecification in Simulation-based Inference through Data-driven Calibrationcited by 26
  8. 2024Multi-marginal Schr"odinger Bridges with Iterative Reference Refinementcited by 26
  9. 2025Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inferencecited by 22
  10. 2022Low-rank Optimal Transport: Approximation, Statistics and Debiasingcited by 21
  11. 2024Learning diffusion at lightspeedcited by 21
  12. 2025Multivariate Conformal Prediction using Optimal Transportcited by 21
  13. 2023Causal Modeling with Stationary Diffusionscited by 19
  14. 2023Self-Consistent Velocity Matching of Probability Flowscited by 17
  15. 2023Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Mapscited by 16
  16. 2022Rethinking Initialization of the Sinkhorn Algorithmcited by 14
  17. 2024Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformerscited by 14
  18. 2022Discovering Conservation Laws using Optimal Transport and Manifold Learningcited by 12
  19. 2024Mirror and Preconditioned Gradient Descent in Wasserstein Spacecited by 11
  20. 2024Progressive Entropic Optimal Transport Solverscited by 10
  21. 2025On Fitting Flow Models with Large Sinkhorn Couplingscited by 10
  22. 2022The derivatives of Sinkhorn-Knopp convergecited by 9
  23. 2023Unbalanced Diffusion Schr"odinger Bridgecited by 9
  24. 2024Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier mapscited by 9
  25. 2024Contrasting Multiple Representations with the Multi-Marginal Matching Gapcited by 8
  26. 2025Hierarchical Refinement: Optimal Transport to Infinity and Beyondcited by 8
  27. 2023Unbalanced Low-rank Optimal Transport Solverscited by 7
  28. 2024Align Your Intents: Offline Imitation Learning via Optimal Transportcited by 7
  29. 2024Low-Rank Optimal Transport through Factor Relaxation with Latent Couplingcited by 7
  30. 2025Rethinking Losses for Diffusion Bridge Samplerscited by 7
  31. 2026FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPUcited by 7
  32. 2022Gaussian Processes on Distributions based on Regularized Optimal Transportcited by 6
  33. 2023Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problemcited by 6
  34. 2023Learning Elastic Costs to Shape Monge Displacementscited by 6
  35. 2024ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transportcited by 6
  36. 2024Annealed Sinkhorn for Optimal Transport: convergence, regularization path and debiasingcited by 6
  37. 2025Learning of Population Dynamics: Inverse Optimization Meets JKO Schemecited by 6
  38. 2022Meta-Learning for Unsupervised Outlier Detection with Optimal Transportcited by 5

50 most cited of 96 All 96 (JSON)