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Maximum likelihood estimation of regularisation parameters in
  high-dimensional inverse problems: an empirical Bayesian approach. Part II:
  Theoretical Analysis

Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis

13 August 2020
Valentin De Bortoli
Alain Durmus
A. F. Vidal
Marcelo Pereyra
ArXiv (abs)PDFHTML

Papers citing "Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis"

10 / 10 papers shown
Title
Sampling Strategies in Bayesian Inversion: A Study of RTO and Langevin
  Methods
Sampling Strategies in Bayesian Inversion: A Study of RTO and Langevin Methods
Remi Laumont
Yiqiu Dong
Martin Skovgaard Andersen
43
1
0
24 Jun 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Hongwei Tan
Ziruo Cai
Marcelo Pereyra
Subhadip Mukherjee
Junqi Tang
Carola-Bibiane Schönlieb
SSL
95
1
0
08 Apr 2024
Proximal Langevin Sampling With Inexact Proximal Mapping
Proximal Langevin Sampling With Inexact Proximal Mapping
Matthias Joachim Ehrhardt
Lorenz Kuger
Carola-Bibiane Schönlieb
61
6
0
30 Jun 2023
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo
  Algorithms
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
Tim Tsz-Kit Lau
Han Liu
Thomas Pock
97
4
0
25 May 2023
The split Gibbs sampler revisited: improvements to its algorithmic
  structure and augmented target distribution
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution
Marcelo Pereyra
L. Mieles
K. Zygalakis
117
7
0
28 Jun 2022
Improving Diffusion Models for Inverse Problems using Manifold
  Constraints
Improving Diffusion Models for Inverse Problems using Manifold Constraints
Hyungjin Chung
Byeongsu Sim
Dohoon Ryu
J. C. Ye
DiffMMedIm
178
473
0
02 Jun 2022
Particle algorithms for maximum likelihood training of latent variable
  models
Particle algorithms for maximum likelihood training of latent variable models
Juan Kuntz
Jen Ning Lim
A. M. Johansen
FedML
109
23
0
27 Apr 2022
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Qiang Heng
Hua Zhou
Eric C. Chi
56
9
0
01 Jan 2022
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
R. Laumont
Valentin De Bortoli
Andrés Almansa
J. Delon
Alain Durmus
Marcelo Pereyra
96
112
0
08 Mar 2021
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo.
  Application to maximum marginal likelihood and empirical Bayesian estimation
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Valentin De Bortoli
Alain Durmus
Marcelo Pereyra
A. F. Vidal
83
33
0
28 Jun 2019
1