{"id":152,"date":"2015-04-22T15:52:28","date_gmt":"2015-04-22T13:52:28","guid":{"rendered":"https:\/\/project.inria.fr\/bnpsi\/?page_id=152"},"modified":"2018-09-18T16:05:43","modified_gmt":"2018-09-18T14:05:43","slug":"publications","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/bnpsi\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<p><\/p>\n<h3>Preprints<\/h3>\n<ul>\n<li>Zacharie Naulet \u00b7 Eric Barat. Bayesian nonparametric estimation for Quantum Homodyne Tomography, preprint Oct 2016.\u00a0 https:\/\/128.84.21.199\/abs\/1610.01895.<\/li>\n<li>A. Todeschini, X. Miscouridou, <a class=\"author\" href=\"http:\/\/mlcs.stats.ox.ac.uk\/people\/caron\/\">F. Caron<\/a>,\u00a0<span id=\"Todeschini2016\">Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities,\u00a0<i>arXiv:1602.02114<\/i>, 2016.\u00a0<\/span><\/li>\n<\/ul>\n<h3>Journals<\/h3>\n<ul>\n<li>B.P. Hejblum, C. Alkhassim, R. Gottardo, F. Caron, R. Thiebaut. Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data. To appear in Annals of Applied Statistics.<\/li>\n<li>F. Caron, E.B. Fox. Sparse graphs using exchangeable random measures.\u00a0Journal of the Royal Statistical Society B (discussion paper), vol. 79, Part 5, pp. 1295-1366, 2017.<\/li>\n<li>J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, &#8220;Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising,&#8221; IEEE Trans. Signal Processing, vol. 65, no. 19, pp. 5215-5224, Oct. 2017.<\/li>\n<li>C. Elvira, P. Chainais, N. Dobigeon. Bayesian anti-sparse coding,\u00a0IEEE Transactions on Signal Processing, vol.\u00a0<span class=\"ng-binding ng-scope\">65<\/span><span class=\"ng-scope\">, no 7, pp.\u00a0<\/span><span class=\"ng-scope\"><span class=\"ng-binding ng-scope\">1660<\/span><span class=\"ng-scope\">&#8211;<\/span><span class=\"ng-binding ng-scope\">1672<\/span><\/span><span class=\"ng-scope\"><span class=\"ng-scope\">,\u00a0<\/span>DOI:\u00a0<a class=\"ng-binding\" href=\"https:\/\/doi.org\/10.1109\/TSP.2016.2645543\">10.1109\/TSP.2016.2645543<\/a>, 2017.<\/span><\/li>\n<li>H.P. Dang, P. Chainais.\u00a0Indian Buffet Process Dictionary Learning : algorithms and applications to image processing, International Journal of Approximate Reasoning, vol. 83, pp 1-20, 2017.<\/li>\n<li>N. Pustelnik, P. Abry, H. Wendt and N. Dobigeon, &#8220;Combining local regularity estimation and total variation optimization for scale-free texture segmentation,&#8221; IEEE Trans. Computational Imaging, vol. 2, no. 4, pp. 468-479, Dec. 2016.<\/li>\n<li>\n<div>J. Prendes, M. Chabert, A. Giros, F. Pascal and\u00a0 J.-Y. Tourneret. A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images, SIAM Journal on Imaging Sciences, vol. 9, no. 4, pp. 1889-1921, 2016.<\/div>\n<\/li>\n<li>\n<div>\n<div>M. Albughdadi, L. Chaari, J.-Y. Tourneret, F. Forbes and P. Ciuciu. A Bayesin Non-Parametric Hidden<\/div>\n<div>Markov Random Model for Hemodynamic Brain Parcellation, to appear in Signal Processing, 2017.<\/div>\n<\/div>\n<\/li>\n<li>\n<div>F. Caron, W. Neiswanger, F. Wood, A. Doucet, M. Davy. Generalized P\u00f3lya urn for time-varying Pitman-Yor processes. Journal of Machine Learning Research, vol. 18(27):1-32, 2017.<\/div>\n<\/li>\n<li>R.\u00a0Bardenet, A.\u00a0Doucet, C.\u00a0Holmes. On Markov chain Monte Carlo methods for tall data, to appear in Journal of Machine Learning Research, 2017.<\/li>\n<li>C. Magnant, A. Giremus, E. Grivel, B. Joseph, L. Ratton, <em>Bayesian non-parametric methods for dynamic state-noise covariance matrix estimation: application to target tracking<\/em>, <a href=\"http:\/\/www.sciencedirect.com\/science\/journal\/01651684\/127\/supp\/C\">Volume 127<\/a>, Octobre 2016, Pages 135\u2013150, Signal Processing Elsevier.<\/li>\n<li>T. Imbiriba, JCM Bermudez, C. Richard, J.-Y. Tourneret, &#8220;Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images,&#8221; IEEE Transactions on Image Processing, 2016, vol. 25, no 3, p. 1136-1151.<\/li>\n<li>M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, and S. McLaughlin, &#8220;A survey of stochastic simulation and optimization methods in signal processing,&#8221; IEEE Journal of Selected Topics in Signal Processing, 2016, vol. 10, no 2, p. 224-241.<\/li>\n<li>H. P. Dang and P. Chainais. Towards dictionaries of optimal size: A bayesian non parametric approach, Journal of Signal Processing Systems, pp. 1\u201312, 2016.\u00a0<abbr title=\"Digital Object Identifier\">DOI<\/abbr>: 10.1007\/s11265-016-1154-1<\/li>\n<li>I. Nevat, G. W. Peters, F. Septier, and T. Matsui, &#8220;Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks,&#8221; IEEE Transactions on Signal Processing, vol. 63, no. 10, pp. 2597-2609, May 2015.<\/li>\n<li>J. Prendes, M. Chabert, F. Pascal, A. Giros, J.-Y. Tourneret, A new multivariate statistical model for change detection\u00a0in images acquired by homogeneous and heterogeneous sensors, IEEE Trans. on Image Process., 2015.<\/li>\n<li>M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, and S. McLaughlin, A survey of\u00a0stochastic simulation and optimization methods in signal processing, IEEE Journal of Selected Topics in Signal\u00a0Processing, vol. PP, no. 99, pp. 1-1, 2015.<\/li>\n<\/ul>\n<h3>International Conferences<\/h3>\n<ul>\n<li>C. Elvira, P. Chainais and N. Dobigeon, &#8220;Bayesian nonparametric subspace estimation,&#8221; in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, 2017, pp. 2247-2251.<\/li>\n<li>J. Sodjo, A. Giremus, N. Dobigeon and J.-F. Giovannelli, &#8220;A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images,&#8221; in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017, pp. 1882-1886.<\/li>\n<li><span class=\"ng-scope\" style=\"color: #000000;\"><span class=\"ng-binding\">H.P. Dang, P. Chainais.\u00a0<\/span><\/span><a class=\"ng-binding ng-scope\" style=\"color: #000000;\" href=\"http:\/\/ieeexplore.ieee.org\/document\/7551729\/\">Indian Buffet process dictionary learning for imag<\/a>e inpainting<span style=\"color: #000000;\">,\u00a0<\/span><a class=\"ng-binding ng-scope\" style=\"color: #000000;\" href=\"http:\/\/ieeexplore.ieee.org\/xpl\/mostRecentIssue.jsp?punumber=7542733\">IEEE Statistical Signal Processing Workshop (SSP)<\/a>\u00a02016<span class=\"ng-scope\"><span class=\"ng-scope\">,\u00a0<\/span>DOI:\u00a0<a class=\"ng-binding\" style=\"color: #000000;\" href=\"https:\/\/doi.org\/10.1109\/SSP.2016.7551729\">10.1109\/SSP.2016.7551729<\/a>.<\/span><\/li>\n<li><span style=\"color: #000000;\"><span class=\"ng-scope\" style=\"color: #000000;\"><span class=\"ng-binding\">C. Elvira, P. Chainais, N. Dobigeon.<\/span><\/span><span class=\"ng-scope\" style=\"color: #000000;\"><span class=\"ng-binding\">\u00a0<\/span><\/span><span style=\"color: #000000;\"><a class=\"ng-binding ng-scope\" href=\"http:\/\/ieeexplore.ieee.org\/document\/7551813\/\">Democratic prior for anti-sparse coding<\/a>, I<\/span><\/span><a class=\"ng-binding ng-scope\" style=\"color: #000000;\" href=\"http:\/\/ieeexplore.ieee.org\/xpl\/mostRecentIssue.jsp?punumber=7542733\">EEE Statistical Signal Processing Workshop (SSP)<\/a>\u00a02016<span class=\"ng-scope\"><span class=\"ng-scope\">,\u00a0<\/span>DOI:\u00a0<a class=\"ng-binding\" style=\"color: #000000;\" href=\"https:\/\/doi.org\/10.1109\/SSP.2016.7551813\">10.1109\/SSP.2016.7551813<\/a>.<\/span><\/li>\n<li>] <a href=\"https:\/\/www.irit.fr\/-Publications-?var_mode=calcul&amp;code=11307&amp;nom=Jessica%20Sodjo\">J. Sodjo<\/a>, <a href=\"https:\/\/www.irit.fr\/-Publications-?var_mode=calcul&amp;code=2706&amp;nom=Audrey%20Giremus\">A. Giremus<\/a>, <a href=\"https:\/\/www.irit.fr\/-Publications-?var_mode=calcul&amp;code=11308&amp;nom=Fran%C3%A7ois%20Caron\">F. Caron<\/a>, <a href=\"https:\/\/www.irit.fr\/-Publications-?var_mode=calcul&amp;code=11309&amp;nom=Jean-Fran%C3%A7ois%20Giovannelli\">J.-F. Giovannelli<\/a>, <a href=\"https:\/\/www.irit.fr\/-Publications-?var_mode=calcul&amp;code=3770&amp;nom=Nicolas%20Dobigeon\">N. Dobigeon<\/a>. <em>Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach<\/em><strong>,<\/strong> IEEE Workshop on statistical signal processing (SSP 2016), <em>Palma de Mallorca<\/em>, Majorque (Bal\u00e9ares), juin 2016.<\/li>\n<li>A. Giremus, V. Pereira, <em>A Bayesian non parametric time-switching autoregressive model for multipath errors in GPS navigation<\/em>, Proc. of 9th IEEE Sensor Array and Multichannel signal processing workshop, SAM 2016, Rio de Janeiro, Br\u00e9sil, Juillet 2016.<\/li>\n<li>J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, &#8220;Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model,&#8221; in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017, pp. 3839-3843.<\/li>\n<li>R. Bardenet, M.K. Titsias, Inference for determinantal point processes without spectral knowledge, Advances in\u00a0Neural Information Proccessing Systems, NIPS 2015.<\/li>\n<li>H.P. Dang, P. Chainais, &#8220;A bayesian non parametric approach to learn dictionaries with adapted numbers of atoms,&#8221; Proceedings of IEEE Int. Conf. on Machine Learning for Signal Processing (MLSP) 2015, Boston. <a href=\"http:\/\/mlsp2015.conwiz.dk\/home.htm\">Intel Best Paper Award<\/a>.<\/li>\n<li>C. Magnant, A. Giremus, E. Grivel, L. Ratton, B. Joseph, Dirichlet process mixture based Bayesian non parametric\u00a0method for Markov switching process estimation, Proceedings of the 23th European Signal Processing Conference,\u00a0EUSIPCO 2015.<\/li>\n<li>C. Magnant, A. Giremus, E. Grivel, L. Ratton, B. Joseph, Joint tracking and classification based on kinematic and\u00a0target extent measurements, Proceedings of Fusion 2015.<\/li>\n<li>J. Prendes, M. Chabert, F. Pascal, A. Giros and J.-Y. Tourneret, &#8220;<a href=\"https:\/\/project.inria.fr\/bnpsi\/files\/2015\/04\/Article_ICASSP_Jorge_Prendes_final.pdf\">Change detection for optical and radar images using a bayesian nonparametric model coupled with a markov random field<\/a>,&#8221; IEEE Proc. of ICASSP 2015.<\/li>\n<li>Naulet, Z. ; Barat, E., pr\u00b4esentation orale `a la 10th Conference on Bayesian Nonparametrics, juin 2015, Duke, NC,\u00a0USA. \u201dAdaptive Bayesian nonparametric regression using mixtures of kernels\u201d.<\/li>\n<li>J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, &#8220;Hybrid Bayesian variational scheme to handle parameter selection in total variation signal denoising,&#8221; in Proc. European Signal Processing Conf. (EUSIPCO), Libon, Portugal, Sep. 2014, pp. 1716-1720.<\/li>\n<li>N. Pustelnik, P. Abry, H. Wendt and N. Dobigeon, &#8220;Inverse problem formulation for regularity estimation in images,&#8221; in Proc. IEEE Int. Conf. Image Processing (ICIP), Paris, France, Oct. 2014, pp. 6081&#8211;6085.<\/li>\n<li>Naulet, Z. ; Barat, E., \u201dSignal stochastic decomposition over continuous dictionaries,\u201d Machine Learning for Signal\u00a0Processing (MLSP), 2014 IEEE International Workshop on , vol., no., pp.1,6, 21-24, MLSP 2014.<\/li>\n<li>N. Jaoua, F. Septier, E. Duflos, and P. Vanheeghe, &#8220;State and Impulsive Time-Varying Measurement Noise Density Estimation in Nonlinear Dynamic Systems Using Dirichlet Process Mixtures,&#8221; in Proc. IEEE Int. Conf. on Acoustic, Speech and signal Porcessing (ICASSP), pp. 330-334, 2014.<\/li>\n<\/ul>\n<h3>National Conferences<\/h3>\n<ul>\n<li>J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, &#8220;S\u00e9lection du param\u00e8tre de r\u00e9gularisation dans le probl\u00e8me l2-Potts,&#8221; in Actes du XXVIi\u00e8me Colloque GRETSI, Sept. 2017.<\/li>\n<li>C. Elvira, P. Chainais and N. Dobigeon, &#8220;Une formulation bay\u00e9sienne du codage antiparcimonieux,&#8221; in Actes du XXVIi\u00e8me Colloque GRETSI, Sept. 2017.<\/li>\n<li>H.P. Dang, P. Chainais, &#8220;Approche bay\u00e9sienne non param\u00e9trique dans l&#8217;apprentissage du dictionnaire pour adapter le nombre d&#8217;atomes,&#8221; Proceedings of the French National Conference GRETSI 2015.<\/li>\n<\/ul>\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Preprints Zacharie Naulet \u00b7 Eric Barat. Bayesian nonparametric estimation for Quantum Homodyne Tomography, preprint Oct 2016.\u00a0 https:\/\/128.84.21.199\/abs\/1610.01895. A. Todeschini, X. Miscouridou, F. Caron,\u00a0Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities,\u00a0arXiv:1602.02114, 2016.\u00a0 Journals B.P. Hejblum, C. Alkhassim, R. Gottardo, F. Caron, R. Thiebaut. Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based &hellip; <\/p>\n<p><a class=\"more-link btn\" href=\"https:\/\/project.inria.fr\/bnpsi\/publications\/\">Continue reading<\/a><\/p>\n","protected":false},"author":424,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_mc_calendar":[],"footnotes":""},"class_list":["post-152","page","type-page","status-publish","hentry","nodate","item-wrap"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/pages\/152","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/users\/424"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/comments?post=152"}],"version-history":[{"count":16,"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/pages\/152\/revisions"}],"predecessor-version":[{"id":264,"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/pages\/152\/revisions\/264"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/bnpsi\/wp-json\/wp\/v2\/media?parent=152"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}