{"total":22,"rows":[{"id":"2311.05672","title":"Conditional Optimal Transport on Function Spaces","created":"2023-11-09","cited_by_count":26,"area":null},{"id":"2202.03926","title":"Distribution Regression with Sliced Wasserstein Kernels","created":"2022-02-08","cited_by_count":18,"area":null},{"id":"2201.10590","title":"Supervised learning of sheared distributions using linearized optimal transport","created":"2022-01-25","cited_by_count":14,"area":null},{"id":"2302.07373","title":"Linearized Wasserstein dimensionality reduction with approximation guarantees","created":"2023-02-14","cited_by_count":12,"area":null},{"id":"2108.08129","title":"Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure","created":"2021-08-18","cited_by_count":11,"area":null},{"id":"2206.07588","title":"Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures","created":"2022-06-15","cited_by_count":7,"area":null},{"id":"2102.12178","title":"Learning to Generate Wasserstein Barycenters","created":"2021-02-24","cited_by_count":6,"area":null},{"id":"2307.01770","title":"Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics","created":"2023-07-04","cited_by_count":5,"area":null},{"id":"2207.14727","title":"Tangential Wasserstein Projections","created":"2022-07-29","cited_by_count":4,"area":null},{"id":"2404.15261","title":"Resistance Distance and Linearized Optimal Transport on Graphs","created":"2024-04-23","cited_by_count":2,"area":null},{"id":"2503.07811","title":"A primer on optimal transport for causal inference with observational data","created":"2025-03-10","cited_by_count":2,"area":null},{"id":"2101.05679","title":"Convex Smoothed Autoencoder-Optimal Transport model","created":"2021-01-14","cited_by_count":1,"area":null},{"id":"2401.01460","title":"Point Cloud Classification via Deep Set Linearized Optimal Transport","created":"2024-01-02","cited_by_count":1,"area":null},{"id":"2410.23602","title":"Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2506.19025","title":"Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2602.04110","title":"Rate-Optimal Noise Annealing in Semi-Dual Neural Optimal Transport: Tangential Identifiability, Off-Manifold Ambiguity, and Guaranteed Recovery","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2603.26940","title":"Static and Dynamic Approaches to Computing Barycenters of Probability Measures on Graphs","created":"2026-03-27","cited_by_count":1,"area":null},{"id":"2202.00954","title":"Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters","created":"2022-02-02","cited_by_count":0,"area":null},{"id":"2310.03945","title":"On Wasserstein distances for affine transformations of random vectors","created":"2023-10-05","cited_by_count":0,"area":null},{"id":"2408.01857","title":"Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems","created":"2024-08-03","cited_by_count":0,"area":null},{"id":"2605.28134","title":"Convergence of empirical subgradients for optimal transport-based objectives","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.23383","title":"Near-Lipschitz stability of the Kim--Milman flow map","created":"2026-06-22","cited_by_count":0,"area":null}],"next_cursor":null}