{"total":15,"rows":[{"id":"2401.03892","title":"Sampling in Unit Time with Kernel Fisher-Rao Flow","created":"2024-01-08","cited_by_count":27,"area":null},{"id":"2310.03597","title":"Sampling via Gradient Flows in the Space of Probability Measures","created":"2023-10-05","cited_by_count":20,"area":null},{"id":"2305.14076","title":"Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent","created":"2023-05-23","cited_by_count":13,"area":null},{"id":"2409.01464","title":"Stein transport for Bayesian inference","created":"2024-09-02","cited_by_count":11,"area":null},{"id":"2406.17263","title":"Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows","created":"2024-06-25","cited_by_count":9,"area":null},{"id":"2410.20622","title":"Kernel Approximation of Fisher-Rao Gradient Flows","created":"2024-10-27","cited_by_count":7,"area":null},{"id":"2506.05905","title":"Sequential Monte Carlo approximations of Wasserstein--Fisher--Rao gradient flows","created":"2025-06-06","cited_by_count":5,"area":null},{"id":"2411.00214","title":"Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics","created":"2024-10-31","cited_by_count":4,"area":null},{"id":"2504.20400","title":"Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow","created":"2025-04-29","cited_by_count":3,"area":null},{"id":"2507.07853","title":"Optimization Guarantees for Square-Root Natural-Gradient Variational Inference","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2509.04008","title":"Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions","created":"2025-09-04","cited_by_count":2,"area":null},{"id":"2605.14142","title":"To discretize continually: Mean shift interacting particle systems for Bayesian inference","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2405.09453","title":"Kuramoto Oscillators and Swarms on Manifolds for Geometry Informed Machine Learning","created":"2024-05-15","cited_by_count":1,"area":null},{"id":"2511.03797","title":"Learning Paths for Dynamic Measure Transport: A Control Perspective","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2602.05227","title":"Radon--Wasserstein Gradient Flows for Interacting-Particle Sampling in High Dimensions","created":"2026-02-05","cited_by_count":1,"area":null}],"next_cursor":null}