Marcelo Pereyra | Publications

Maxwell Insitute for Mathematical Sciences
School of Mathematical and Computer Sciences, Heriot-Watt University

Complete publication list:google scholar logoGoogle Scholar profile

Journal Papers

[31]   X. Cai, J.D. McEwen, M. Pereyra, "High-dimensional Bayesian model selection by proximal nested sampling". Pre-print https://arxiv.org/abs/2106.03646.
[30]   M. Holden, M. Pereyra, K. Zygalakis, "Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks", SIAM Journal on Imaging Sciences, to appear. Pre-print https://arxiv.org/abs/2103.10182.
[29]   R. Laumont, V. de Bortoli, A. Almansa, J. Delon, A. Durmus, and M. Pereyra, "Bayesian imaging using Plug and Play priors: when Langevin meets Tweedie", to appear in SIAM Journal on Imaging Sciences. Preprint https://arxiv.org/abs/2103.04715.
[28]   R. Laumont, V. de Bortoli, A. Almansa, J. Delon, A. Durmus, and M. Pereyra, "On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent", 2021. Preprint https://hal.archives-ouvertes.fr/hal-03348735/.
[27]   V. De Bortoli, A. Durmus, A. F. Vidal, M. Pereyra, "Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part II: Theoretical Analysis", SIAM Journal on Imaging Sciences, vol. 13, no. 4, pp. 1990-2028, 2020. https://doi.org/10.1137/20M1339842.
[26]   A. F. Vidal, V. De Bortoli, M. Pereyra, A. Durmus, "Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments", SIAM Journal on Imaging Sciences, vol. 13, no. 4, pp. 1945-1989, 2020. https://doi.org/10.1137/20M1339829.
[25]   V. De Bortoli, A. F. Vidal, A. Durmus, M. Pereyra, "Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation", Statistics and Computing, vol. 31, no. 29., 2021.    https://doi.org/10.1007/s11222-020-09986-y.
[24]   K. S. Zhang, G. Peyré, J. Fadili, M. Pereyra, "Wasserstein Control of Mirror Langevin Monte Carlo", COLT 2020, PMLR 125:3814-3841, 2020. http://proceedings.mlr.press/v125/zhang20a.html.
[23]   L. Vargas, M. Pereyra, K. Zygalakis, "Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods", SIAM Journal on Imaging Sciences, Vol. 13, No. 2, pp. 905–935. https://epubs.siam.org/doi/abs/10.1137/19M1283719.
[22]   M. A. Price, X. Cai, J.D. McEwen, M. Pereyra, T. D. Kitching, "Sparse Bayesian mass-mapping with uncertainties: local credible intervals", Monthly Notices of the Royal Astronomical Society, vol. 492, no. 1, 2020, pp. 394–404. https://doi.org/10.1093/mnras/stz3453.
[21]   M. Pereyra, "Revisiting maximum-a-posteriori estimation in log-concave models", SIAM Journal on Imaging Sciences, vol. 12, no. 1, pp. 650-670. March 2019. https://doi.org/10.1137/18M1174076
[20]   A. Repetti, M. Pereyra, Y. Wiaux "Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization", SIAM Journal on Imaging Sciences, vol. 12, no. 1,, pp. 87-118. March 2019. https://doi.org/10.1137/18M1173629
[19]   A. Quintero-Rincon, M. Pereyra, C. D'Giano, H. Batatia, M. Risk "Fast statistical model-based classification of epileptic EEG signals", Biocybernetics and Biomedical Engineering, Vol. 38, No. 4, pp. 877-889. Aug. 2018. https://doi.org/10.1016/j.bbe.2018.08.002.
[18]   X. Cai, M. Pereyra, J.D. McEwen "Uncertainty quantification for radio interferometric imaging. Part I: Proximal MCMC methods", Monthly Notices of the Royal Astronomical Society, July 2018. https://doi.org/10.1093/mnras/sty2004
[17]   X. Cai, M. Pereyra, J.D. McEwen "Uncertainty quantification for radio interferometric imaging. Part II: MAP Estimation", Monthly Notices of the Royal Astronomical Society, July 2018. https://doi.org/10.1093/mnras/sty2015
[16]   A. Durmus, E. Moulines, M. Pereyra, "Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau", SIAM Journal on Imaging Sciences, vol. 11, no. 1, 473-506. Mar. 2018. https://doi.org/10.1137/16M1108340
[15]   N. Brosse, A. Durmus, E. Moulines, M. Pereyra, "Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo", COLT 2017, PMLR 65:319-342, 2017. http://proceedings.mlr.press/v65/brosse17a.html
[14]   M. Pereyra, "Maximum-a-posteriori estimation with Bayesian confidence regions", SIAM Journal on Imaging Sciences, vol. 10, no. 1, 285–302. Feb. 2017. https://doi.org/10.1137/16M1071249
[13]   M. Pereyra, S. McLaughlin, "Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation", IEEE Trans. Image Processing, vol. 26, no. 6, pp. 2577 - 2587, Jun. 2017. https://doi.org/10.1109/TIP.2017.2675165 (open access)
[12]   T. Norton, M. Pereyra, M. Knight, B. Mcgarry, K. T. Jokivarsi, O. H. J. Grohn, R. Kauppinen,"Stroke Onset Time Determination Using MRI Relaxation Times without Non-Ischaemic Reference in A Rat Stroke Model", Biomedical Spectroscopy and Imaging, vol. 6, no. 1-2, pp. 25-35, Jun. 2017.
[11]   M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, S. McLaughlin, "A Survey on Stochastic Simulation and Optimization Methods in Signal Processing" IEEE Sel. Topics in Signal Processing vol. 10, no. 2, pp 224 - 241, Mar. 2016 | open access download | http://arxiv.org/abs/1505.00273
[10]   Y. Altmann, M. Pereyra, S. McLaughlin, "Bayesian nonlinear hyperspectral unmixing with spatial residual component analysis," IEEE Trans. Computational Imaging, vol. 1, no. 3, pp 174 - 185, Sep. 2015 | open access download | http://arxiv.org/abs/1412.4681
[9]   Y. Altmann, M. Pereyra, J. Bioucas-Dias, "Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing," IEEE Trans. Image Processing, vol. 24, no. 12, pp 5800 - 5811, Dec. 2015 | open access download | http://arxiv.org/abs/1409.8129
[8]   P. Green, K. Łatuszynski, M. Pereyra, C. P. Robert, "Bayesian computation: a perspective on the current state, and sampling backwards and forwards," Statistics and Computing, vol. 25, no. 4, pp 835-862, Jul. 2015 | open access download | http://arxiv.org/abs/1502.01148
[7]   M. Pereyra, "Proximal Markov chain Monte Carlo algorithms," Statistics and Computing, vol. 26, no. 4, pp 745–760, Jul. 2016 | open access download | http://arxiv.org/abs/1306.0187
[6]   M. Pereyra, H. Batatia and S. McLaughlin, "Exploiting information geometry to improve the convergence properties of nonparametric active contours," IEEE Trans. Image Processing, vol. 24, no. 3, Mar. 2015 | open access download
[5]   M. Pereyra, N. Dobigeon, H. Batatia and J.-Y. Tourneret, "Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models," IEEE Signal Processing Letters, vol. 21, no. 1, pp. 47-50, Jan. 2014 | http://arxiv.org/abs/1206.3985
[4] M. Pereyra, H. Batatia and S. McLaughlin, "Exploiting information geometry to improve the convergence properties of variational active contours," IEEE Sel. Topics in Signal Processing, vol. 7, no. 4, pp. 1-8, Aug. 2013.
[3] M. Pereyra, N. Dobigeon, H. Batatia and J.-Y. Tourneret, "Estimating the Granularity Coefficient of a Potts-Markov Random field within an MCMC algorithm," IEEE Trans. Image Processing, vol. 22, no. 6, pp. 2385-2397, June 2013 | https://arxiv.org/abs/1207.5355
[2] M. Pereyra, N. Dobigeon, H. Batatia and J.-Y. Tourneret, "Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model," IEEE Trans. Medical Imaging, vol. 31, no. 8., pp. 1509-1520, Aug. 2012.
[1] M. Pereyra and H. Batatia, "Modeling ultrasound echoes in skin tissues using symmetric α-stable processes," IEEE Trans. Ultrasonics, Ferroelectrics and Frequency Control, vol. 59, no. 1, pp. 60 - 72, Jan. 2012.

© 2017 Marcelo Pereyra
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