Marcelo Pereyra
Orcid: 0000-0001-6438-6772
According to our database1,
Marcelo Pereyra
authored at least 57 papers
between 2011 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
IEEE Signal Process. Lett., 2024
2023
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution.
SIAM J. Imaging Sci., December, 2023
SIAM J. Imaging Sci., September, 2023
On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent.
J. Math. Imaging Vis., January, 2023
Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications.
IEEE Signal Process. Mag., 2023
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging.
CoRR, 2023
Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems.
CoRR, 2023
2022
SIAM J. Imaging Sci., June, 2022
A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau.
SIAM Rev., 2022
SIAM J. Imaging Sci., 2022
Stat. Comput., 2022
2021
Stat. Comput., 2021
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms.
CoRR, 2021
Proceedings of the IEEE Statistical Signal Processing Workshop, 2021
Bayesian Model Selection for Unsupervised Image Deconvolution with Structured Gaussian Priors.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2021
2020
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments.
SIAM J. Imaging Sci., 2020
Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method.
SIAM J. Imaging Sci., 2020
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis.
SIAM J. Imaging Sci., 2020
Proceedings of the Conference on Learning Theory, 2020
2019
Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization.
SIAM J. Imaging Sci., 2019
SIAM J. Imaging Sci., 2019
CoRR, 2019
Proceedings of the 27th European Signal Processing Conference, 2019
2018
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau.
SIAM J. Imaging Sci., 2018
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018
Proceedings of the 26th European Signal Processing Conference, 2018
Uncertainty Quantification in Imaging: When Convex Optimization Meets Bayesian Analysis.
Proceedings of the 26th European Signal Processing Conference, 2018
2017
IEEE Trans. Image Process., 2017
SIAM J. Imaging Sci., 2017
CoRR, 2017
Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods.
CoRR, 2017
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo.
Proceedings of the 30th Conference on Learning Theory, 2017
2016
IEEE J. Sel. Top. Signal Process., 2016
Introduction to the Issue on Stochastic Simulation and Optimization in Signal Processing.
IEEE J. Sel. Top. Signal Process., 2016
Proceedings of the IEEE Statistical Signal Processing Workshop, 2016
Proceedings of the IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, 2016
Proceedings of the 24th European Signal Processing Conference, 2016
2015
Exploiting Information Geometry to Improve the Convergence of Nonparametric Active Contours.
IEEE Trans. Image Process., 2015
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing.
IEEE Trans. Image Process., 2015
IEEE Trans. Computational Imaging, 2015
Bayesian computation: a summary of the current state, and samples backwards and forwards.
Stat. Comput., 2015
CoRR, 2015
Linear spectral unmixing using collaborative sparse regression and correlated supports.
Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2015
Proceedings of the 23rd European Signal Processing Conference, 2015
Nonlinear spectral unmixing using residual component analysis and a Gamma Markov random field.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015
2014
Computing the Cramer-Rao Bound of Markov Random Field Parameters: Application to the Ising and the Potts Models.
IEEE Signal Process. Lett., 2014
Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm.
Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014
Small-variance asymptotics of hidden Potts-MRFS: Application to fast Bayesian image segmentation.
Proceedings of the 22nd European Signal Processing Conference, 2014
2013
Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm.
IEEE Trans. Image Process., 2013
Exploiting Information Geometry to Improve the Convergence Properties of Variational Active Contours.
IEEE J. Sel. Top. Signal Process., 2013
2012
Statistical modeling and processing of high frequency ultrasound images: Application to dermatologic oncology. (Modélisation et traitement statistiques d'images d'ultrasons de haute fréquence. Application à l'oncologie dermatologique).
PhD thesis, 2012
Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model.
IEEE Trans. Medical Imaging, 2012
2011
Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model.
Proceedings of the IEEE International Conference on Acoustics, 2011
Segmentation of ultrasound images using a spatially coherent generalized Rayleigh mixture model.
Proceedings of the 19th European Signal Processing Conference, 2011