{"list":"other","total":54,"rows":[{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"1601.00670","title":"Variational Inference: A Review for Statisticians","created":"2016-01-04","cited_by_count":1472},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"1608.04471","title":"Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm","created":"2016-08-16","cited_by_count":507},{"id":"1605.09522","title":"Kernel Mean Embedding of Distributions: A Review and Beyond","created":"2016-05-31","cited_by_count":364},{"id":"1602.03253","title":"A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation","created":"2016-02-10","cited_by_count":236},{"id":"1807.02582","title":"Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences","created":"2018-07-06","cited_by_count":192},{"id":"1704.07520","title":"Stein Variational Gradient Descent as Gradient Flow","created":"2017-04-25","cited_by_count":152},{"id":"1602.02964","title":"A Kernel Test of Goodness of Fit","created":"2016-02-09","cited_by_count":143},{"id":"1508.07941","title":"Optimal Entropy-Transport problems and a new Hellinger-Kantorovich distance between positive measures","created":"2015-08-31","cited_by_count":139},{"id":"2103.12959","title":"Solving and Learning Nonlinear PDEs with Gaussian Processes","created":"2021-03-24","cited_by_count":129},{"id":"1906.04370","title":"Maximum Mean Discrepancy Gradient Flow","created":"2019-06-11","cited_by_count":127},{"id":"1703.01717","title":"Measuring Sample Quality with Kernels","created":"2017-03-06","cited_by_count":119},{"id":"2006.00702","title":"Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation","created":"2020-06-01","cited_by_count":88},{"id":"1705.10958","title":"FALKON: An Optimal Large Scale Kernel Method","created":"2017-05-31","cited_by_count":84},{"id":"1912.00894","title":"On the geometry of Stein variational gradient descent","created":"2019-12-02","cited_by_count":82},{"id":"1604.04054","title":"Optimal Rates For Regularization Of Statistical Inverse Learning Problems","created":"2016-04-14","cited_by_count":68},{"id":"2006.02509","title":"SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence","created":"2020-06-03","cited_by_count":64},{"id":"2006.09797","title":"A Non-Asymptotic Analysis for Stein Variational Gradient Descent","created":"2020-06-17","cited_by_count":61},{"id":"1410.2392","title":"Control functionals for Monte Carlo integration","created":"2014-10-09","cited_by_count":59},{"id":"2105.09994","title":"Kernel Stein Discrepancy Descent","created":"2021-05-20","cited_by_count":57},{"id":"1310.7780","title":"The Information Geometry of Mirror Descent","created":"2013-10-29","cited_by_count":54},{"id":"1806.03085","title":"A Stein variational Newton method","created":"2018-06-08","cited_by_count":50},{"id":"1711.04425","title":"Message Passing Stein Variational Gradient Descent","created":"2017-11-13","cited_by_count":47},{"id":"2407.18163","title":"Statistical optimal transport","created":"2024-07-25","cited_by_count":47},{"id":"1905.09863","title":"Accelerating Langevin Sampling with Birth-death","created":"2019-05-23","cited_by_count":45},{"id":"2407.16212","title":"Optimal experimental design: Formulations and computations","created":"2024-07-23","cited_by_count":45},{"id":"1805.04035","title":"Scaling limit of the Stein variational gradient descent: the mean field regime","created":"2018-05-10","cited_by_count":35},{"id":"2105.03481","title":"Stein's Method Meets Computational Statistics: A Review of Some Recent Developments","created":"2021-05-07","cited_by_count":32},{"id":"2211.00450","title":"Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics","created":"2022-11-01","cited_by_count":30},{"id":"2301.01766","title":"Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow","created":"2023-01-04","cited_by_count":27},{"id":"2401.03892","title":"Sampling in Unit Time with Kernel Fisher-Rao Flow","created":"2024-01-08","cited_by_count":27},{"id":"2405.06672","title":"Liouville Flow Importance Sampler","created":"2024-05-03","cited_by_count":25},{"id":"2001.04341","title":"Information Newton's flow: second-order optimization method in probability space","created":"2020-01-13","cited_by_count":24},{"id":"2006.16531","title":"Sliced Kernelized Stein Discrepancy","created":"2020-06-30","cited_by_count":23},{"id":"2310.02951","title":"A Fisher-Rao gradient flow for entropy-regularised Markov decision processes in Polish spaces","created":"2023-10-04","cited_by_count":23},{"id":"2310.03597","title":"Sampling via Gradient Flows in the Space of Probability Measures","created":"2023-10-05","cited_by_count":20},{"id":"2211.07861","title":"Regularized Stein Variational Gradient Flow","created":"2022-11-15","cited_by_count":19},{"id":"1812.00071","title":"Stochastic Gradient MCMC with Repulsive Forces","created":"2018-11-30","cited_by_count":18},{"id":"2302.11024","title":"Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance","created":"2023-02-21","cited_by_count":16},{"id":"2302.12229","title":"An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow","created":"2023-02-23","cited_by_count":16},{"id":"2310.11914","title":"A connection between Tempering and Entropic Mirror Descent","created":"2023-10-18","cited_by_count":16},{"id":"2102.12956","title":"Stein Variational Gradient Descent: many-particle and long-time asymptotics","created":"2021-02-25","cited_by_count":15},{"id":"2202.03297","title":"Grassmann Stein Variational Gradient Descent","created":"2022-02-07","cited_by_count":12},{"id":"2401.12967","title":"Measure transport with kernel mean embeddings","created":"2024-01-23","cited_by_count":11},{"id":"1412.5845","title":"Poisson's equation in nonlinear filtering","created":"2014-12-18","cited_by_count":10},{"id":"2405.17075","title":"Interaction-Force Transport Gradient Flows","created":"2024-05-27","cited_by_count":9},{"id":"2010.11779","title":"Measure Transport with Kernel Stein Discrepancy","created":"2020-10-22","cited_by_count":8},{"id":"2104.03384","title":"Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods","created":"2021-04-07","cited_by_count":8},{"id":"2007.12658","title":"McKean-Vlasov SDEs in nonlinear filtering","created":"2020-07-24","cited_by_count":7},{"id":"1902.07263","title":"Diffusion map-based algorithm for Gain function approximation in the Feedback Particle Filter","created":"2019-02-19","cited_by_count":5},{"id":"2307.15496","title":"From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs","created":"2023-07-28","cited_by_count":5}],"next_cursor":null}