Multifractal analysis and α-stable processes: a methodological contribution. - Pierre Chainais; Patrice Abry; Darryl Veitch (ICASSP, 2000)

Infinitely divisible cascade analysis of network traffic data. - Darryl Veitch; Patrice Abry; Patrick Flandrin; Pierre Chainais (ICASSP, 2000)

Scaling exponents estimation for multiscaling processes. - Bruno Lashermes; Patrice Abry; Pierre Chainais (ICASSP (2), 2004)

New Insights into the Estimation of Scaling Exponents. - Bruno Lashermes; Patrice Abry; Pierre Chainais (Int. J. Wavelets Multiresolution Inf. Process., 2004)

Multi-dimensional infinitely divisible cascades to model the statistics of natural images. - Pierre Chainais (ICIP (3), 2005)

On non-scale-invariant infinitely divisible cascades. - Pierre Chainais; Rudolf H. Riedi; Patrice Abry (IEEE Trans. Inf. Theory, 2005)

Modelling switching dynamics using prediction experts operating on distinct wavelet scales. - Alexandre Aussem; Pierre Chainais (ESANN, 2006)

Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system. - Zahra Hamou Mamar; Pierre Chainais; Alexandre Aussem (ESANN, 2006)

Infinitely Divisible Cascades to Model the Statistics of Natural Images. - Pierre Chainais (IEEE Trans. Pattern Anal. Mach. Intell., 2007)

Multifractal Analysis on the Sphere. - Emilie Koenig; Pierre Chainais (ICISP, 2008)

Processus aléatoires invariants d'échelle et analyse multirésolution pour la modélisation d'observations de systèmes physiques. (Scale invariant stochastic processes and multiresolution analysis for the modeling of physical systems). - Pierre Chainais (2009)

Virtual resolution enhancement of scale invariant textured images using stochastic processes. - Emilie Koenig; Pierre Chainais (ICIP, 2009)

Multifractal random walks as fractional Wiener integrals. - Patrice Abry; Pierre Chainais; Laure Coutin; Vladas Pipiras (IEEE Trans. Inf. Theory, 2009)

Scale invariant images in astronomy through the lens of multifractal modeling. - Pierre Chainais; Véronique Delouille; Jean-François Hochedez (ICIP, 2011)

Virtual Super Resolution of Scale Invariant Textured Images Using Multifractal Stochastic Processes. - Pierre Chainais; Emilie Koenig; Véronique Delouille; Jean-François Hochedez (J. Math. Imaging Vis., 2011)

Towards dictionary learning from images with non Gaussian noise. - Pierre Chainais (MLSP, 2012)

Learning a common dictionary over a sensor network. - Pierre Chainais; Cédric Richard (CAMSAP, 2013)

Distributed dictionary learning over a sensor network - Pierre Chainais; Cédric Richard (CoRR, 2013)

Quantitative control of the error bounds of a fast super-resolution technique for microscopy and astronomy. - Pierre Chainais; Pierre Pfennig; Aymeric Leray (ICASSP, 2014)

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). - Laurent Jacques; Christophe De Vleeschouwer; Yannick Boursier; Prasad Sudhakar; C. De Mol; Aleksandra Pizurica; Sandrine Anthoine; Pierre Vandergheynst; Pascal Frossard; Cagdas Bilen; Srdan Kitic; Nancy Bertin; et al. (CoRR, 2014)

Dictionary learning for a sparse appearance model in visual tracking. - Sylvain Rousseau 001; Pierre Chainais; Christelle Garnier (ICIP, 2015)

A Bayesian non parametric approach to learn dictionaries with adapted numbers of atoms. - Hong Phuong Dang; Pierre Chainais (MLSP, 2015)

Indian Buffet process dictionary learning for image inpainting. - Hong Phuong Dang; Pierre Chainais (SSP, 2016)

Democratic prior for anti-sparse coding. - Clement Elvira; Pierre Chainais; Nicolas Dobigeon (SSP, 2016)

Statistical Performance Analysis of a Fast Super-Resolution Technique Using Noisy Translations. - Pierre Chainais; Aymeric Leray (IEEE Trans. Image Process., 2016)

Bayesian nonparametric subspace estimation. - Clement Elvira; Pierre Chainais; Nicolas Dobigeon (ICASSP, 2017)

Polarization spectrogram of bivariate signals. - Julien Flamant; Pierre Chainais; Nicolas Le Bihan (ICASSP, 2017)

Indian Buffet Process dictionary learning: Algorithms and applications to image processing. - Hong Phuong Dang; Pierre Chainais (Int. J. Approx. Reason., 2017)

Bayesian Antisparse Coding. - Clement Elvira; Pierre Chainais; Nicolas Dobigeon (IEEE Trans. Signal Process., 2017)

Spectral Analysis of Stationary Random Bivariate Signals. - Julien Flamant; Nicolas Le Bihan; Pierre Chainais (IEEE Trans. Signal Process., 2017)

Small variance asymptotics and bayesian nonparametrics for dictionary learning. - Clement Elvira; Hong Phuong Dang; Pierre Chainais (EUSIPCO, 2018)

Non-parametric characterization of gravitational-wave polarizations. - Julien Flamant; Pierre Chainais; Éric Chassande-Mottin; Fangchen Feng; Nicolas Le Bihan (EUSIPCO, 2018)

Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (MLSP, 2018)

Linear Filtering of Bivariate Signals Using Quaternions. - Julien Flamant; Pierre Chainais; Nicolas Le Bihan (SSP, 2018)

A Complete Framework for Linear Filtering of Bivariate Signals. - Julien Flamant; Pierre Chainais; Nicolas Le Bihan (IEEE Trans. Signal Process., 2018)

Towards Dictionaries of Optimal Size: A Bayesian Non Parametric Approach. - Hong Phuong Dang; Pierre Chainais (J. Signal Process. Syst., 2018)

Bayesian Image Restoration under Poisson Noise and Log-concave Prior. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (ICASSP, 2019)

Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (ICASSP, 2019)

Kernel quadrature with DPPs. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (NeurIPS, 2019)

A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data. - Maxime Vono; Javier R. Goicoechea; Pierre Gratier; Viviana V. Guzmán; Annie Hughes; Jouni Kainulainen; David Languignon; Jacques Le Bourlot; François Levrier; Harvey S. Listz; Karin I. Oberg; Emeric Bron; et al. (WHISPERS, 2019)

Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (IEEE Trans. Signal Process., 2019)

Kernel interpolation with continuous volume sampling. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (ICML, 2020)

A determinantal point process for column subset selection. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (J. Mach. Learn. Res., 2020)

Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (J. Comput. Graph. Stat., 2021)

Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. - Florentin Coeurdoux; Nicolas Dobigeon; Pierre Chainais (ESANN, 2022)

Mixture of noises and sampling of non-log-concave posterior distributions. - Pierre Palud; Pierre Chainais; Franck Le Petit; Emeric Bron; Pierre-Antoine Thouvenin; Maxime Vono; L. Einig; M. Garcia Santa-Maria; Mathilde Gaudel; Jan H. Orkisz; Victor de Souza Magalhaes; Sébastien Bardeau; et al. (EUSIPCO, 2022)

A versatile distributed MCMC algorithm for large scale inverse problems. - Pierre-Antoine Thouvenin; Audrey Repetti; Pierre Chainais (EUSIPCO, 2022)

Fast Fusion of Hyperspectral and Multispectral Images: A Tucker Approximation Approach. - Clémence Prévost; Pierre Chainais; Rémy Boyer (ICIP, 2022)

Learning Optimal Transport Between Two Empirical Distributions with Normalizing Flows. - Florentin Coeurdoux; Nicolas Dobigeon; Pierre Chainais (ECML/PKDD (5), 2022)

A distributed Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems. - Pierre-Antoine Thouvenin; Audrey Repetti; Pierre Chainais (CoRR, 2022)

High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (SIAM Rev., 2022)

Signal reconstruction using determinantal sampling. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (CoRR, 2023)

Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises. - Pierre Palud; Pierre-Antoine Thouvenin; Pierre Chainais; Emeric Bron; Franck Le Petit (IEEE Trans. Signal Process., 2023)

Quaternions in Signal and Image Processing: A comprehensive and objective overview. - Sebastian Miron; Julien Flamant; Nicolas Le Bihan; Pierre Chainais; David Brie (IEEE Signal Process. Mag., 2023)

Denoising Bivariate Signals via Smoothing and Polarization Priors. - Yusuf Yigit Pilavci; Jérémie Boulanger; Pierre-Antoine Thouvenin; Pierre Chainais (EUSIPCO, 2024)

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems. - Markus Grimm; Sébastien Paul; Pierre Chainais (Comput. Chem. Eng., 2024)

Benchmarking multi-component signal processing methods in the time-frequency plane. - Juan Manuel Miramont; Rémi Bardenet; Pierre Chainais; François Auger (CoRR, 2024)

A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems. - Pierre-Antoine Thouvenin; Audrey Repetti; Pierre Chainais (J. Comput. Graph. Stat., 2024)

Plug-and-Play Split Gibbs Sampler: Embedding Deep Generative Priors in Bayesian Inference. - Florentin Coeurdoux; Nicolas Dobigeon; Pierre Chainais (IEEE Trans. Image Process., 2024)

Normalizing flow sampling with Langevin dynamics in the latent space. - Florentin Coeurdoux; Nicolas Dobigeon; Pierre Chainais (Mach. Learn., 2024)

Multi-GPU distributed PnP-ULA for high-dimensional imaging inverse problems. - Maxime Bouton; Pierre-Antoine Thouvenin; Audrey Repetti; Pierre Chainais (SSP, 2025)

Adaptive hyperparameter tuning for time-frequency algorithms based on the zeros of the spectrogram. - Juan Manuel Miramont; Rémi Bardenet; Pierre Chainais; François Auger (SSP, 2025)

Time and covariance smoothing for restoration of bivariate signals. - Yusuf Yigit Pilavci; Pierre Palud; Julien Flamant; Pierre-Antoine Thouvenin; Jérémie Boulanger; Pierre Chainais (SSP, 2025)

Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains. - Clémence Prévost; Pierre Chainais (Signal Process., 2025)