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On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms

11 September 2020

Papers citing "On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms"

27 / 27 papers shown

Title
Valid Credible Ellipsoids for Linear Functionals by a Renormalized Bernstein-von Mises Theorem Gustav Rømer 28 0 0 17 Sep 2024
Convergence Rates for the Maximum A Posteriori Estimator in PDE-Regression Models with Random Design Maximilian Siebel 44 1 0 05 Sep 2024
On the Frequentist Coverage of Bayes Posteriors in Nonlinear Inverse Problems You-Hyun Baek Katerina Papagiannouli Sayan Mukherjee 54 0 0 19 Jul 2024
The Laplace asymptotic expansion in high dimensions Anya Katsevich 57 2 0 18 Jun 2024
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems Lorenzo Baldassari Ali Siahkoohi Josselin Garnier K. Sølna Maarten V. de Hoop DiffM 124 2 0 24 May 2024
Statistical algorithms for low-frequency diffusion data: A PDE approach Matteo Giordano Sven Wang 69 4 0 02 May 2024
Bayesian Nonparametric Inference in McKean-Vlasov models Richard Nickl G. Pavliotis Kolyan Ray 37 5 0 25 Apr 2024
Early Stopping for Ensemble Kalman-Bucy Inversion Maia Tienstra 16 0 0 27 Mar 2024
Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models Filippo Ascolani Gareth O. Roberts Giacomo Zanella 73 6 0 14 Mar 2024
Deep Gaussian Process Priors for Bayesian Inference in Nonlinear Inverse Problems Kweku Abraham Neil Deo 41 5 0 21 Dec 2023
Statistical guarantees for stochastic Metropolis-Hastings S. Bieringer Gregor Kasieczka Maximilian F. Steffen Mathias Trabs 80 1 0 13 Oct 2023
Distribution learning via neural differential equations: a nonparametric statistical perspective Youssef Marzouk Zhi Ren Sven Wang Jakob Zech 82 12 0 03 Sep 2023
Improved dimension dependence in the Bernstein von Mises Theorem via a new Laplace approximation bound A. Katsevich 56 5 0 14 Aug 2023
On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations Richard Nickl E. Titi 37 8 0 16 Jul 2023
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation Jose Antonio Lara Benitez Takashi Furuya F. Faucher Anastasis Kratsios X. Tricoche Maarten V. de Hoop 114 19 0 27 Jan 2023
Consistent inference for diffusions from low frequency measurements Richard Nickl 52 8 0 24 Oct 2022
Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin Yiping Lu Jose H. Blanchet Lexing Ying 102 13 0 28 Sep 2022
On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions Afonso S. Bandeira Antoine Maillard Richard Nickl Sven Wang 65 10 0 05 Sep 2022
Besov-Laplace priors in density estimation: optimal posterior contraction rates and adaptation M. Giordano 25 5 0 30 Aug 2022
Polynomial time guarantees for sampling based posterior inference in high-dimensional generalised linear models R. Altmeyer 56 4 0 28 Aug 2022
A Bernstein--von-Mises theorem for the Calderón problem with piecewise constant conductivities Jan Bohr 57 2 0 16 Jun 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 100 8 0 15 May 2022
Laplace priors and spatial inhomogeneity in Bayesian inverse problems S. Agapiou Sven Wang 115 15 0 10 Dec 2021
On some information-theoretic aspects of non-linear statistical inverse problems Richard Nickl G. Paternain 79 9 0 20 Jul 2021
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems Jan Bohr Richard Nickl 47 17 0 17 May 2021
Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem Hanne Kekkonen 44 12 0 24 Mar 2021
Statistical guarantees for Bayesian uncertainty quantification in non-linear inverse problems with Gaussian process priors F. Monard Richard Nickl G. Paternain 49 36 0 31 Jul 2020