Publications

Preprints

  • Zacharie Naulet · Eric Barat. Bayesian nonparametric estimation for Quantum Homodyne Tomography, preprint Oct 2016.  https://128.84.21.199/abs/1610.01895.
  • A. Todeschini, X. Miscouridou, F. Caron, Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, arXiv:1602.02114, 2016. 

Journals

  • 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.
  • F. Caron, E.B. Fox. Sparse graphs using exchangeable random measures. Journal of the Royal Statistical Society B (discussion paper), vol. 79, Part 5, pp. 1295-1366, 2017.
  • J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, “Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising,” IEEE Trans. Signal Processing, vol. 65, no. 19, pp. 5215-5224, Oct. 2017.
  • C. Elvira, P. Chainais, N. Dobigeon. Bayesian anti-sparse coding, IEEE Transactions on Signal Processing, vol. 65, no 7, pp. 1660–1672, DOI: 10.1109/TSP.2016.2645543, 2017.
  • H.P. Dang, P. Chainais. Indian Buffet Process Dictionary Learning : algorithms and applications to image processing, International Journal of Approximate Reasoning, vol. 83, pp 1-20, 2017.
  • N. Pustelnik, P. Abry, H. Wendt and N. Dobigeon, “Combining local regularity estimation and total variation optimization for scale-free texture segmentation,” IEEE Trans. Computational Imaging, vol. 2, no. 4, pp. 468-479, Dec. 2016.
  • J. Prendes, M. Chabert, A. Giros, F. Pascal and  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.
  • M. Albughdadi, L. Chaari, J.-Y. Tourneret, F. Forbes and P. Ciuciu. A Bayesin Non-Parametric Hidden
    Markov Random Model for Hemodynamic Brain Parcellation, to appear in Signal Processing, 2017.
  • F. Caron, W. Neiswanger, F. Wood, A. Doucet, M. Davy. Generalized Pólya urn for time-varying Pitman-Yor processes. Journal of Machine Learning Research, vol. 18(27):1-32, 2017.
  • R. Bardenet, A. Doucet, C. Holmes. On Markov chain Monte Carlo methods for tall data, to appear in Journal of Machine Learning Research, 2017.
  • C. Magnant, A. Giremus, E. Grivel, B. Joseph, L. Ratton, Bayesian non-parametric methods for dynamic state-noise covariance matrix estimation: application to target tracking, Volume 127, Octobre 2016, Pages 135–150, Signal Processing Elsevier.
  • T. Imbiriba, JCM Bermudez, C. Richard, J.-Y. Tourneret, “Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images,” IEEE Transactions on Image Processing, 2016, vol. 25, no 3, p. 1136-1151.
  • M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, and S. McLaughlin, “A survey of stochastic simulation and optimization methods in signal processing,” IEEE Journal of Selected Topics in Signal Processing, 2016, vol. 10, no 2, p. 224-241.
  • H. P. Dang and P. Chainais. Towards dictionaries of optimal size: A bayesian non parametric approach, Journal of Signal Processing Systems, pp. 1–12, 2016. DOI: 10.1007/s11265-016-1154-1
  • I. Nevat, G. W. Peters, F. Septier, and T. Matsui, “Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks,” IEEE Transactions on Signal Processing, vol. 63, no. 10, pp. 2597-2609, May 2015.
  • J. Prendes, M. Chabert, F. Pascal, A. Giros, J.-Y. Tourneret, A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors, IEEE Trans. on Image Process., 2015.
  • M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, and S. McLaughlin, A survey of stochastic simulation and optimization methods in signal processing, IEEE Journal of Selected Topics in Signal Processing, vol. PP, no. 99, pp. 1-1, 2015.

International Conferences

  • C. Elvira, P. Chainais and N. Dobigeon, “Bayesian nonparametric subspace estimation,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, 2017, pp. 2247-2251.
  • J. Sodjo, A. Giremus, N. Dobigeon and J.-F. Giovannelli, “A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017, pp. 1882-1886.
  • H.P. Dang, P. Chainais. Indian Buffet process dictionary learning for image inpainting, IEEE Statistical Signal Processing Workshop (SSP) 2016, DOI: 10.1109/SSP.2016.7551729.
  • C. Elvira, P. Chainais, N. Dobigeon. Democratic prior for anti-sparse coding, IEEE Statistical Signal Processing Workshop (SSP) 2016, DOI: 10.1109/SSP.2016.7551813.
  • ] J. Sodjo, A. Giremus, F. Caron, J.-F. Giovannelli, N. Dobigeon. Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach, IEEE Workshop on statistical signal processing (SSP 2016), Palma de Mallorca, Majorque (Baléares), juin 2016.
  • A. Giremus, V. Pereira, A Bayesian non parametric time-switching autoregressive model for multipath errors in GPS navigation, Proc. of 9th IEEE Sensor Array and Multichannel signal processing workshop, SAM 2016, Rio de Janeiro, Brésil, Juillet 2016.
  • J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, “Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017, pp. 3839-3843.
  • R. Bardenet, M.K. Titsias, Inference for determinantal point processes without spectral knowledge, Advances in Neural Information Proccessing Systems, NIPS 2015.
  • H.P. Dang, P. Chainais, “A bayesian non parametric approach to learn dictionaries with adapted numbers of atoms,” Proceedings of IEEE Int. Conf. on Machine Learning for Signal Processing (MLSP) 2015, Boston. Intel Best Paper Award.
  • C. Magnant, A. Giremus, E. Grivel, L. Ratton, B. Joseph, Dirichlet process mixture based Bayesian non parametric method for Markov switching process estimation, Proceedings of the 23th European Signal Processing Conference, EUSIPCO 2015.
  • C. Magnant, A. Giremus, E. Grivel, L. Ratton, B. Joseph, Joint tracking and classification based on kinematic and target extent measurements, Proceedings of Fusion 2015.
  • J. Prendes, M. Chabert, F. Pascal, A. Giros and J.-Y. Tourneret, “Change detection for optical and radar images using a bayesian nonparametric model coupled with a markov random field,” IEEE Proc. of ICASSP 2015.
  • Naulet, Z. ; Barat, E., pr´esentation orale `a la 10th Conference on Bayesian Nonparametrics, juin 2015, Duke, NC, USA. ”Adaptive Bayesian nonparametric regression using mixtures of kernels”.
  • J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, “Hybrid Bayesian variational scheme to handle parameter selection in total variation signal denoising,” in Proc. European Signal Processing Conf. (EUSIPCO), Libon, Portugal, Sep. 2014, pp. 1716-1720.
  • N. Pustelnik, P. Abry, H. Wendt and N. Dobigeon, “Inverse problem formulation for regularity estimation in images,” in Proc. IEEE Int. Conf. Image Processing (ICIP), Paris, France, Oct. 2014, pp. 6081–6085.
  • Naulet, Z. ; Barat, E., ”Signal stochastic decomposition over continuous dictionaries,” Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on , vol., no., pp.1,6, 21-24, MLSP 2014.
  • N. Jaoua, F. Septier, E. Duflos, and P. Vanheeghe, “State and Impulsive Time-Varying Measurement Noise Density Estimation in Nonlinear Dynamic Systems Using Dirichlet Process Mixtures,” in Proc. IEEE Int. Conf. on Acoustic, Speech and signal Porcessing (ICASSP), pp. 330-334, 2014.

National Conferences

  • J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt and P. Abry, “Sélection du paramètre de régularisation dans le problème l2-Potts,” in Actes du XXVIième Colloque GRETSI, Sept. 2017.
  • C. Elvira, P. Chainais and N. Dobigeon, “Une formulation bayésienne du codage antiparcimonieux,” in Actes du XXVIième Colloque GRETSI, Sept. 2017.
  • H.P. Dang, P. Chainais, “Approche bayésienne non paramétrique dans l’apprentissage du dictionnaire pour adapter le nombre d’atomes,” Proceedings of the French National Conference GRETSI 2015.