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Marcelo Pereyra
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2020 – today
- 2024
- [j31]Teresa Klatzer
, Paul Dobson, Yoann Altmann
Accelerated Bayesian Imaging by Relaxed Proximal-Point Langevin Sampling. SIAM J. Imaging Sci. 17(2): 1078-1117 (2024) - [j30]Charlesquin Kemajou Mbakam
Marginal Likelihood Estimation in Semiblind Image Deconvolution: A Stochastic Approximation Approach. SIAM J. Imaging Sci. 17(2): 1206-1254 (2024) - [j29]Savvas Melidonis
Empirical Bayesian Imaging With Large-Scale Push-Forward Generative Priors. IEEE Signal Process. Lett. 31: 631-635 (2024) - [c20]Marcelo Pereyra, Julián Tachella:
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems. AISTATS 2024: 4141-4149 - [i17]Hong Ye Tan, Ziruo Cai, Marcelo Pereyra, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation. CoRR abs/2404.05445 (2024) - [i16]David Y. W. Thong, Charlesquin Kemajou Mbakam, Marcelo Pereyra:
Do Bayesian imaging methods report trustworthy probabilities? CoRR abs/2405.08179 (2024) - [i15]Charlesquin Kemajou Mbakam, Jean-François Giovannelli, Marcelo Pereyra:
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior. CoRR abs/2409.04384 (2024) - [i14]Abdul-Lateef Haji-Ali, Marcelo Pereyra, Luke Shaw, Konstantinos Zygalakis:
Bayesian computation with generative diffusion models by Multilevel Monte Carlo. CoRR abs/2409.15511 (2024) - 2023
- [j28]Rémi Laumont
On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent. J. Math. Imaging Vis. 65(1): 140-163 (2023) - [j27]Savvas Melidonis
Efficient Bayesian Computation for Low-Photon Imaging Problems. SIAM J. Imaging Sci. 16(3): 1195-1234 (2023) - [j26]Marcelo Pereyra
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution. SIAM J. Imaging Sci. 16(4): 2040-2071 (2023) - [j25]Subhadip Mukherjee
Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications. IEEE Signal Process. Mag. 40(1): 164-182 (2023) - [i13]Teresa Klatzer, Paul Dobson, Yoann Altmann
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling. CoRR abs/2308.09460 (2023) - [i12]Julián Tachella, Marcelo Pereyra:
Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems. CoRR abs/2310.11838 (2023) - [i11]Tobías I. Liaudat
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging. CoRR abs/2312.00125 (2023) - 2022
- [j24]Xiaohao Cai
Proximal nested sampling for high-dimensional Bayesian model selection. Stat. Comput. 32(5): 87 (2022) - [j23]Rémi Laumont
Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie. SIAM J. Imaging Sci. 15(2): 701-737 (2022) - [j22]Matthew Holden, Marcelo Pereyra
Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks. SIAM J. Imaging Sci. 15(2): 892-924 (2022) - [j21]Alain Durmus, Éric Moulines, Marcelo Pereyra
A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau. SIAM Rev. 64(4): 991-1028 (2022) - [i10]Rémi Laumont, Valentin De Bortoli, Andrés Almansa
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent. CoRR abs/2201.06133 (2022) - [i9]Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Carola-Bibiane Schönlieb:
Learned reconstruction with convergence guarantees. CoRR abs/2206.05431 (2022) - 2021
- [j20]Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal:
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Stat. Comput. 31(3): 29 (2021) - [c19]Ana Fernandez Vidal, Marcelo Pereyra, Alain Durmus, Jean-François Giovannelli:
Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals. SSP 2021: 91-95 - [c18]Benjamin Harroué, Jean-François Giovannelli, Marcelo Pereyra:
Bayesian Model Selection for Unsupervised Image Deconvolution with Structured Gaussian Priors. SSP 2021: 241-245 - [i8]Rémi Laumont, Valentin De Bortoli, Andrés Almansa
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. CoRR abs/2103.04715 (2021) - [i7]Matthew Holden, Marcelo Pereyra, Konstantinos C. Zygalakis:
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms. CoRR abs/2103.10182 (2021) - 2020
- [j19]Marcelo Pereyra
Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method. SIAM J. Imaging Sci. 13(2): 905-935 (2020) - [j18]Ana Fernandez Vidal
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments. SIAM J. Imaging Sci. 13(4): 1945-1989 (2020) - [j17]Valentin De Bortoli, Alain Durmus, Marcelo Pereyra
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis. SIAM J. Imaging Sci. 13(4): 1990-2028 (2020) - [c17]Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra:
Wasserstein Control of Mirror Langevin Monte Carlo. COLT 2020: 3814-3841 - [i6]Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra:
Wasserstein Control of Mirror Langevin Monte Carlo. CoRR abs/2002.04363 (2020)
2010 – 2019
- 2019
- [j16]Audrey Repetti
Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization. SIAM J. Imaging Sci. 12(1): 87-118 (2019) - [j15]Marcelo Pereyra
Revisiting Maximum-A-Posteriori Estimation in Log-Concave Models. SIAM J. Imaging Sci. 12(1): 650-670 (2019) - [c16]Xiaohao Cai
Quantifying Uncertainty in High Dimensional Inverse Problems by Convex Optimisation. EUSIPCO 2019: 1-5 - [i5]Luis Vargas
Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods. CoRR abs/1908.08845 (2019) - 2018
- [j14]Alain Durmus, Eric Moulines, Marcelo Pereyra:
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. SIAM J. Imaging Sci. 11(1): 473-506 (2018) - [c15]Julián Tachella, Yoann Altmann
Bayesian Restoration of High-Dimensional Photon-Starved Images. EUSIPCO 2018: 747-751 - [c14]Audrey Repetti
Uncertainty Quantification in Imaging: When Convex Optimization Meets Bayesian Analysis. EUSIPCO 2018: 2668-2672 - [c13]Ana Fernandez Vidal, Marcelo Pereyra:
Maximum Likelihood Estimation of Regularisation Parameters. ICIP 2018: 1742-1746 - 2017
- [j13]Marcelo Pereyra:
Maximum-a-Posteriori Estimation with Bayesian Confidence Regions. SIAM J. Imaging Sci. 10(1): 285-302 (2017) - [j12]Marcelo Pereyra
Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation. IEEE Trans. Image Process. 26(6): 2577-2587 (2017) - [c12]Nicolas Brosse, Alain Durmus, Eric Moulines, Marcelo Pereyra:
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. COLT 2017: 319-342 - [i4]Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen:
Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods. CoRR abs/1711.04818 (2017) - [i3]Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen:
Uncertainty quantification for radio interferometric imaging: II. MAP estimation. CoRR abs/1711.04819 (2017) - 2016
- [j11]Steve McLaughlin
Introduction to the Issue on Stochastic Simulation and Optimization in Signal Processing. IEEE J. Sel. Top. Signal Process. 10(2): 221-223 (2016) - [j10]Marcelo Pereyra, Philip Schniter
A Survey of Stochastic Simulation and Optimization Methods in Signal Processing. IEEE J. Sel. Top. Signal Process. 10(2): 224-241 (2016) - [j9]Marcelo Pereyra:
Proximal Markov chain Monte Carlo algorithms. Stat. Comput. 26(4): 745-760 (2016) - [c11]Marcelo Pereyra, Steve McLaughlin
Comparing Bayesian models in the absence of ground truth. EUSIPCO 2016: 528-532 - [c10]Antonio Quintero-Rincón
Multivariate Bayesian classification of epilepsy EEG signals. IVMSP 2016: 1-5 - [c9]Marcelo Pereyra:
Approximating Bayesian confidence regions in convex inverse problems. SSP 2016: 1-5 - 2015
- [j8]Peter J. Green, Krzysztof Latuszynski, Marcelo Pereyra, Christian P. Robert
Bayesian computation: a summary of the current state, and samples backwards and forwards. Stat. Comput. 25(4): 835-862 (2015) - [j7]Yoann Altmann
Bayesian Nonlinear Hyperspectral Unmixing With Spatial Residual Component Analysis. IEEE Trans. Computational Imaging 1(3): 174-185 (2015) - [j6]Marcelo Pereyra, Hadj Batatia, Steve McLaughlin
Exploiting Information Geometry to Improve the Convergence of Nonparametric Active Contours. IEEE Trans. Image Process. 24(3): 836-845 (2015) - [j5]Yoann Altmann
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing. IEEE Trans. Image Process. 24(12): 5800-5811 (2015) - [c8]Yoann Altmann
Nonlinear spectral unmixing using residual component analysis and a Gamma Markov random field. CAMSAP 2015: 165-168 - [c7]Marcelo Pereyra, José M. Bioucas-Dias
Maximum-a-posteriori estimation with unknown regularisation parameters. EUSIPCO 2015: 230-234 - [c6]Yoann Altmann
Linear spectral unmixing using collaborative sparse regression and correlated supports. WHISPERS 2015: 1-4 - [i2]Marcelo Pereyra, Steve McLaughlin:
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation. CoRR abs/1502.01400 (2015) - [i1]Marcelo Pereyra, Philip Schniter, Emilie Chouzenoux, Jean-Christophe Pesquet, Jean-Yves Tourneret, Alfred O. Hero III, Steve McLaughlin
Tutorial on Stochastic Simulation and Optimization Methods in Signal Processing. CoRR abs/1505.00273 (2015) - 2014
- [j4]Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret:
Computing the Cramer-Rao Bound of Markov Random Field Parameters: Application to the Ising and the Potts Models. IEEE Signal Process. Lett. 21(1): 47-50 (2014) - [c5]Marcelo Pereyra, Steve McLaughlin:
Small-variance asymptotics of hidden Potts-MRFS: Application to fast Bayesian image segmentation. EUSIPCO 2014: 1597-1601 - [c4]Marcelo Pereyra, Nick Whiteley
Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. SSP 2014: 121-124 - 2013
- [j3]Marcelo Pereyra, Hadj Batatia, Steve McLaughlin
Exploiting Information Geometry to Improve the Convergence Properties of Variational Active Contours. IEEE J. Sel. Top. Signal Process. 7(4): 700-707 (2013) - [j2]Marcelo Pereyra, Nicolas Dobigeon
Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm. IEEE Trans. Image Process. 22(6): 2385-2397 (2013) - [c3]Marcelo Pereyra, Hadj Batatia, Steve McLaughlin
Exploiting information geometry to improve the convergence of nonparametric active contours. CAMSAP 2013: 165-168 - 2012
- [b1]Marcelo Pereyra:
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). National Polytechnic Institute of Toulouse, France, 2012 - [j1]Marcelo Pereyra, Nicolas Dobigeon
Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model. IEEE Trans. Medical Imaging 31(8): 1509-1520 (2012) - 2011
- [c2]Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret:
Segmentation of ultrasound images using a spatially coherent generalized Rayleigh mixture model. EUSIPCO 2011: 664-668 - [c1]Marcelo Pereyra, Nicolas Dobigeon
Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model. ICASSP 2011: 729-732
Coauthor Index
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last updated on 2024-10-22 21:18 CEST by the dblp team
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