Marcelo Pereyra | DeepAI

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Andreas Hauptmann
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Xiaohao Cai
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Jason D. McEwen
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∙ 08/18/2023

Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling

This paper presents a new accelerated proximal Markov chain Monte Carlo ...

0 Teresa Klatzer, et al. ∙

∙ 06/30/2023

Proximal nested sampling with data-driven priors for physical scientists

Proximal nested sampling was introduced recently to open up Bayesian mod...

0 Jason D. McEwen, et al. ∙

∙ 06/28/2022

The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution

This paper proposes a new accelerated proximal Markov chain Monte Carlo ...

6 Marcelo Pereyra, et al. ∙

∙ 06/11/2022

Learned reconstruction with convergence guarantees

In recent years, deep learning has achieved remarkable empirical success...

16 Subhadip Mukherjee, et al. ∙

∙ 06/10/2022

Efficient Bayesian computation for low-photon imaging problems

This paper studies a new and highly efficient Markov chain Monte Carlo (...

0 Savvas Melidonis, et al. ∙

∙ 01/16/2022

On Maximum-a-Posteriori estimation with Plug Play priors and stochastic gradient descent

Bayesian methods to solve imaging inverse problems usually combine an ex...

7 Rémi Laumont, et al. ∙

∙ 06/07/2021

High-dimensional Bayesian model selection by proximal nested sampling

Imaging methods often rely on Bayesian statistical inference strategies ...

0 Xiaohao Cai, et al. ∙

∙ 03/18/2021

Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms

This paper proposes a new methodology for performing Bayesian inference ...

22 Matthew Holden, et al. ∙

∙ 03/08/2021

Bayesian imaging using Plug Play priors: when Langevin meets Tweedie

Since the seminal work of Venkatakrishnan et al. (2013), Plug Play (...

10 Rémi Laumont, et al. ∙

∙ 10/13/2020

Bayesian model selection for unsupervised image deconvolution with structured Gaussian priors

This paper considers the objective comparison of stochastic models to so...

0 Benjamin Harroué, et al. ∙

∙ 08/13/2020

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

This paper presents a detailed theoretical analysis of the three stochas...

0 Valentin De Bortoli, et al. ∙

∙ 02/11/2020

Wasserstein Control of Mirror Langevin Monte Carlo

Discretized Langevin diffusions are efficient Monte Carlo methods for sa...

0 Kelvin Shuangjian Zhang, et al. ∙

∙ 11/26/2019

Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach

Many imaging problems require solving an inverse problem that is ill-con...

0 Ana F. Vidal, et al. ∙

∙ 08/23/2019

Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods

We present a highly efficient proximal Markov chain Monte Carlo methodol...

0 Luis Vargas, et al. ∙

∙ 06/28/2019

Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation

Stochastic approximation methods play a central role in maximum likeliho...

0 Valentin De Bortoli, et al. ∙

∙ 03/02/2018

Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization

We propose a Bayesian uncertainty quantification method for large-scale ...

0 Audrey Repetti, et al. ∙

∙ 11/13/2017

Uncertainty quantification for radio interferometric imaging: II. MAP estimation

Uncertainty quantification is a critical missing component in radio inte...

0 Xiaohao Cai, et al. ∙

∙ 11/13/2017

Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods

Uncertainty quantification is a critical missing component in radio inte...

0 Xiaohao Cai, et al. ∙

∙ 02/05/2015

Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation

This paper presents a new Bayesian estimation technique for hidden Potts...

0 Marcelo Pereyra, et al. ∙

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