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We introduce a new mean-field ODE and corresponding interacting particle systems (IPS) for sampling from an unnormalized target density.
Polar factorization and monotone rearrangement of vector-valued functions
Brenier, Y · 1991
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Markov chain Monte Carlo maximum likelihood
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Exponential convergence of langevin distributions and their discrete approximations
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Partial differential equations and Monge-Kantorovich mass transfer
Evans, L. C · 1997
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Annealed importance sampling
Neal, R. M · 2001
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Slice sampling
Neal, R. M · 2003
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The Metropolis—Hastings algorithm
Robert, C. P. and Casella, G · 2004
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Parallel tempering: Theory, applications, and new perspectives
Earl, D. J. and Deem, M. W · 2005
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Recursive Monte Carlo filters: Algorithms and theoretical analysis
Künsch, H. R · 2005
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Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference
Baptista, R., Hosseini, B., Kovachki, N. B., and Marzouk, Y · 2006
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Sequential Monte Carlo samplers
Del Moral, P., Doucet, A., and Jasra, A · 2006
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Random Features for Large-Scale Kernel Machines
Rahimi, A. and Recht, B · 2007
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Obstacles to high-dimensional particle filtering
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J · 2008
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Kernels and Reproducing Kernel Hilbert Spaces , pp. 110–163
Steinwart, I. and Christmann, A · 2008
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On the representation and learning of monotone triangular transport maps
Baptista, R., Marzouk, Y., and Zahm, O · 2009
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Particle flow for nonlinear filters
Daum, F. and Huang, J · 2011
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A dynamical systems framework for intermittent data assimilation
Reich, S · 2011
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Annealed Importance Sampling with q-Paths
Brekelmans, R., Masrani, V., Bui, T., Wood, F., Galstyan, A., Steeg, G. V., and Nielsen, F · 2012
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Particle flow for nonlinear filters, Bayesian decisions and transport
Daum, F. and Huang, J · 2013
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Ensemble Kalman methods for inverse problems
Iglesias, M. A., Law, K. J. H., and Stuart, A. M · 2013
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Monte Carlo theory, methods and examples
Owen, A. B · 2013
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A Nonparametric Ensemble Transform Method for Bayesian Inference
Reich, S · 2013
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Poisson’s equation in nonlinear filtering
Laugesen, R. S., Mehta, P. G., Meyn, S. P., and Raginsky, M · 2015
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Probabilistic forecasting and Bayesian data assimilation
Reich, S. and Cotter, C · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Information geometry and its applications , volume 194
Amari, S.-i · 2016
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Robust benchmarking in noisy environments
Chen, J. and Revels, J · 2016
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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Liu, Q. and Wang, D · 2016
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Sampling via measure transport: An introduction
Marzouk, Y., Moselhy, T., Parno, M., and Spantini, A · 2016
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Data-Driven Optimal Transport
Trigila, G. and Tabak, E. G · 2016
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DifferentialEquations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia
Rackauckas, C. and Nie, Q · 2017
Parallel tempering on optimized paths
Syed, S., Romaniello, V., Campbell, T., and Bouchard-Côté, A · 2021
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Topics in Optimal Transportation
Villani, C · 2021
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Supervised training of conditional Monge maps
Bunne, C., Krause, A., and Cuturi, M · 2022
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Consensus-based sampling
Carrillo, J. A., Hoffmann, F., Stuart, A. M., and Vaes, U · 2022
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Adaptive importance sampling meets mirror descent: a bias-variance tradeoff
Korba, A. and Portier, F · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2022
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Inference via low-dimensional couplings
Spantini, A., Bigoni, D., and Marzouk, Y · 2018
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Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions
Chen, P., Wu, K., Chen, J., O’Leary-Roseberry, T., and Ghattas, O · 2019
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Sum-of-squares polynomial flow
Jaini, P., Selby, K. A., and Yu, Y · 2019
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Sample-Based Optimal Transport and Barycenter Problems
Kuang, M. and Tabak, E. G · 2019
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Transform-based particle filtering for elliptic Bayesian inverse problems
Ruchi, S., Dubinkina, S., and Iglesias, M. A · 2019
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Greedy inference with structure-exploiting lazy maps
Brennan, M. C., Bigoni, D., Zahm, O., Spantini, A., and Marzouk, Y · 2020
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Coupling Techniques for Nonlinear Ensemble Filtering
Spantini, A., Baptista, R., and Marzouk, Y · 2022
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An Optimal Transport Formulation of Bayes’ Law for Nonlinear Filtering Algorithms
Taghvaei, A. and Hosseini, B · 2022
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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
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Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance, July 2023
Chen, Y., Huang, D. Z., Huang, J., Reich, S., and Stuart, A. M · 2023
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Log-Concave Sampling
Chewi, S · 2023
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A Continuation Method in Bayesian Inference
Dia, B. M · 2023
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An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow
Domingo-Enrich, C. and Pooladian, A.-A · 2023
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Adaptive annealed importance sampling with constant rate progress
Goshtasbpour, S., Cohen, V., and Perez-Cruz, F · 2023
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Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields
Katzfuss, M. and Schäfer, F · 2023
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Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2023
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A survey of feedback particle filter and related controlled interacting particle systems (CIPS)
Taghvaei, A. and Mehta, P. G · 2023
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From optimization to sampling through gradient flows
Trillos, N. G., Hosseini, B., and Sanz-Alonso, D · 2023
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Denoising Diffusion Samplers
Vargas, F., Grathwohl, W. S., and Doucet, A · 2023
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Diffusion schrödinger bridges for bayesian computation
Heng, J., De Bortoli, V., and Doucet, A · 2024
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Liouville Flow Importance Sampler
Tian, Y., Panda, N., and Lin, Y. T · 2024
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Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Vargas, F., Padhy, S., Blessing, D., and Nüsken, N · 2024
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Measure transport with kernel mean embeddings
Wang, L. and Nüsken, N · 2024
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Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Zhang, D., Chen, R. T. Q., Liu, C.-H., Courville, A., and Bengio, Y · 2024
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