Overview

Optimal transport studies how probability mass can be moved between distributions at minimum cost, giving a geometric way to compare data even when samples do not correspond one-to-one. Wasserstein distances and transport maps describe this geometry, while Gromov–Wasserstein methods compare relational structure when data live in different spaces. Practical approaches include entropic regularization with Sinkhorn algorithms, sliced and low-rank approximations, and neural transport maps used in generative modeling, domain adaptation, and trajectory inference. A central challenge is balancing computational and statistical scalability with faithful transport, especially when choosing regularization or relaxing exact mass constraints.

Latest papers

CardsList

289 papers48 papers in the last four weeks, up 17% on the four weeks before. 0.3% of all new papers.

  1. Conditional Sampling via Wasserstein Autoencoders and Triangular Transport

    Apr 3, 2026Mohammad Al-Jarrah, Michele Martino, Marcus Yim +2Conditional Generative ModelingOptimal Transport

  2. Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport

    Apr 3, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteMultidimensional ScalingMulti-View Learning

  3. HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation

    Apr 2, 2026Ba-Thinh Nguyen, Thi-Duyen Ngo, Thanh-Trung Huynh +2Remote PhotoplethysmographyOptimal Transport

  4. Causal Optimal Coupling for Gaussian Input-Output Distributional Data

    Apr 1, 2026Daran Xu, Amirhossein TaghvaeiOptimal TransportSchrödinger Bridges

  5. An Explicit Surrogate for Gaussian Mixture Flow Matching with Wasserstein Gap Bounds

    Mar 30, 2026Elham Rostami, Taous-Meriem Laleg-Kirati, Hamidou TembineFlow MatchingOptimal Transport

  6. Static and Dynamic Approaches to Computing Barycenters of Probability Measures on Graphs

    Mar 27, 2026David Gentile, James M. MurphyWasserstein BarycentersOptimal Transport

  7. Wasserstein Parallel Transport for Predicting the Dynamics of Statistical Systems

    Mar 24, 2026Tristan Luca Saidi, Gonzalo Mena, Larry Wasserman +1Batch Effect CorrectionCausal Counterfactual Generation

  8. Sinkhorn Based Associative Memory Retrieval Using Spherical Hellinger Kantorovich Dynamics

    Mar 21, 2026Aratrika Mustafi, Soumya MukherjeeSinkhorn DivergenceAssociative Memory

  9. SuCor: Susceptibility Distortion Correction via Parameter-Free and Self-Regularized Optimal Transport

    Mar 17, 2026Sreekar Chigurupati, Eleftherios GaryfallidisMagnetic Resonance ImagingNeuroimaging

  10. Persistence Spheres: a Bi-continuous Linear Representation of Measures for Partial Optimal Transport

    Mar 16, 2026Matteo PegoraroTopological Representation LearningPersistent Homology

  11. Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling

    Mar 16, 2026Aram Davtyan, Leello Tadesse Dadi, Volkan Cevher +1Flow-Based Generative ModelingConditional Flow Matching

  12. Beyond Distance: Quantifying Point Cloud Dynamics with Persistent Homology and Dynamic Optimal Transport

    Mar 15, 2026Yixin Wang, Ting Gao, Jinqiao DuanTopological Data AnalysisOptimal Transport

  13. Sampling Boltzmann distributions via normalizing flow approximation of transport maps

    Mar 15, 2026Zia Ur Rehman, Gero FrieseckeBoltzmann SamplingNormalizing Flows

  14. Sobolev--Ricci Curvature

    Mar 13, 2026Kyoichi Iwasaki, Tam Le, Hideitsu HinoOptimal Transport

  15. Sinkhorn-Drifting Generative Models

    Mar 12, 2026Ping He, Om Khangaonkar, Hamed Pirsiavash +2Sinkhorn DivergenceDrifting Models

  16. Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

    Mar 12, 2026Jiin Im, Sisung Liu, Je Hyeong HongSemantic CorrespondenceOptimal Transport

  17. COT-FM: Cluster-wise Optimal Transport Flow Matching

    Mar 11, 2026Chiensheng Chiang, Kuan-Hsun Tu, Jia-Wei Liao +2Flow MatchingGenerative Modeling

  18. Unbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering

    Mar 10, 2026Joshua Lentz, Nicholas Karris, Alex Cloninger +1Image ClusteringDictionary Learning

  19. Towards Effective and Efficient Graph Alignment without Supervision

    Mar 9, 2026Songyang Chen, Youfang Lin, Yu Liu +2Graph MatchingGraph Representation Learning

  20. Conditional Unbalanced Optimal Transport Maps: An Outlier-Robust Framework for Conditional Generative Modeling

    Mar 7, 2026Jiwoo Yoon, Kyumin Choi, Jaewoong ChoiOptimal TransportConditional Generative Modeling

  21. From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows

    Mar 5, 2026Cenwei Zhang, Lin Zhu, Manxi Lin +1Feature AttributionShapley Value Attribution

  22. Transport Clustering: Solving Low-Rank Optimal Transport via Clustering

    Mar 3, 2026Henri Schmidt, Peter Halmos, Ben RaphaelOptimal TransportClustering

  23. Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport

    Mar 2, 2026Harry Amad, Mihaela van der SchaarNeural Surrogate ModelingOptimal Transport

  24. Multimodal Optimal Transport for Training-free Temporal Segmentation in Surgical Robotics

    Feb 27, 2026Omar Mohamed, Edoardo Fazzari, Ayah Al-Naji +5Temporal Action SegmentationSurgical Video Understanding

  25. Neural solver for Wasserstein Geodesics and optimal transport dynamics

    Feb 25, 2026Hailiang Liu, Yan-Han ChenOptimal Transport

  26. Entropy-Controlled Flow Matching

    Feb 25, 2026Chika MaduabuchiFlow MatchingFlow-Based Generative Modeling

  27. Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents

    Feb 23, 2026Michael ChertkovDiffusion ModelsMulti-Agent Control