Recent publications

More Publications

(2021). Deconvolution with unknown noise distribution is possible for multivariate signals. To appear in The Annals of Statistics.


(2021). Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. Submitted.


(2021). End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort. Energy and Buildings, 250:1.

Preprint Source Document

(2021). NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform. Submitted.


(2021). Joint self-supervised blind denoising and noise estimation. Submitted.


(2020). Learning the distribution of latent variables in paired comparison models. Bernoulli, volume 26, number 4, p. 2670-2698.

Preprint Source Document

(2020). The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction. Submitted.


(2020). Backward importance sampling for online estimation of state space models. Submitted.


(2020). Identifiability and consistent estimation of nonparametric translation hidden Markov models with general state space. The Journal of Machine Learning Research (JMLR), (115):1-40, 2020.

Preprint PDF Source Document

(2019). A pseudo-marginal sequential Monte Carlo online smoothing algorithm. Submitted.


(2018). Optimizing thermal comfort and energy consumption in a large building without renovation work. IEEE Data Science Workshop, p.41-45, EPFL, Lausanne.

Preprint Source Document

(2018). Stochastic differential equation based on a multimodal potential to model movement data in ecology. Journal of the Royal Statistical Society: Series C, Volume 67, Number 3, p. 599-616.

Preprint Source Document

(2018). On the two-filter approximations of marginal smoothing distributions in general state space models. Advances in Applied Probability, Volume 50, Number 1, p. 154-177.

Preprint Source Document

(2018). Online sequential Monte Carlo smoother for partially observed diffusion processes. EURASIP Journal on Advances in Signal Processing, Volume 9.

Preprint Source Document

Research activities

Current Ph.D. students

  • Alice Martin (Telecom SudParis & Ecole Polytechnique, Apr. 2019 - …)
    Co-supervised with Olivier Pietquin (Google Brain)
    Deep reinforcement learning for natural language processing. Application to goal-oriented visual question generation
  • Max Cohen (Telecom SudParis & Oze Energies, Feb. 2020 - …)
    Co-supervised with Marius Preda (Telecom SudParis)
    Metamodels and Bayesian deep learning for partially observed dynamical systems
  • Yazid Janati (Telecom SudParis, Oct. 2020 - …)
    Co-supervised with Yohan Petetin (Telecom SudParis)
    On the combination of deep learning models with Monte Carlo methods
  • Mathis Chagneux (Telecom Paris, Nov. 2020 - …)
    Co-supervised with Pierre gloaguen (Agro ParisTech) and Charles Ollion (Ecole Polytechnique)
    Computer vision and statistical models for the detection and tracking of plastic wastes
  • Etienne David (Telecom SudParis & Heuritech, Feb. 2021 - …)
    Co-supervised with Jean Bellot (Heuritech)
    Hybrid models and weak signals to forecast fashion trends

Algorithms & Simulations

Notebooks, Matlab codes, R Markdown associated with scientific publications

Data sciences material

Publications about working group sessions at TSP


Supervision activities (internships, Ms.Sc., Ph.D., fellowships)


Lecture notes

Introduction to Markov chains and Markov chain Monte Carlo methods.
Ms. Sc. Data Sciences (2nd year / M2) @Institut Polytechnique de Paris

Introduction to machine learning.
Ms. Eng. (2nd year) @Institut Polytechnique de Paris
Dimension reduction, supervised classification, multilinear regression, gradient based optimization…

Bayesian inference for partially observed dynamical systems
Ms. Sc. Data Sciences (2nd year / M2) @Institut Polytechnique de Paris

Markov chains Exercises

Maximum likelihood estimation for Markov chains Exercises

Markov Chain Monte Carlo methods Exercises

Asymptotic properties of Markov chains Exercises

Metropolis-Hastings, MALA, Hamiltonian Monte Carlo Practical session (Python ipynb)

Introduction to machine learning Ms. Eng. (2nd year) @Institut Polytechnique de Paris

Dimension reduction: Principal component analysis, independent component analysis
Slides and Practical session (Python ipynb)

Supervised classification (I): Bayes classifier, discriminant analysis, Support vector machines
Slides and Practical session (Python ipynb)

Supervised classification (II): Logistic regression, Feed forward neural networks
Slides and Practical session (Python ipynb)

Gradient descent based optimization algorithms
Slides and Practical session (Python ipynb)

Kernel based regression and random forests
Practical session (Python ipynb)

Introduction to computational statistics, machine learning and deep learning

Ateliers SFDS 2019: MCMC and introduction to Hamiltonian Monte Carlo HMC, ABC and variational (Python ipynb)

Dimension reduction (SVD, PCA, use before machine learning algorithms) PCA (Python ipynb).

Introduction to machine learning (random forest, gradient descent, feed forward neural networks) Intro to machine learning (Python ipynb).

Machine learning algorithms for the Ms. Sc. Advanced Machine learning (3rd year) @Ecole Polytechnique

AdaBoost and Random forests pdf.

Kernel Principal Component Analysis pdf.

Kmeans and Expectation Maximization algorithms pdf.

Machine learning algorithms for the Ms. Sc. Machine learning (2nd year) @TSP

Singular value decomposition for image reconstruction svd (Python ipynb).

Linear and quadratic discriminant analysis lda (R Markdown).

Support Vector Machines - application to well being at work data svm (Python ipynb).

Simulation of stochatic differential equations SDE (Python ipynb).

Advanced Statistics exercises for the Ms. Sc. MA2822 (2nd year) @Centrale Supelec

Introduction to simulation and Monte Carlo methods pdf.

Kernel based density estimators pdf.

Stochastic calculus Ms. Sc. Machine learning (2nd year) @TSP

Lecture notes on stochastic differential equations (v0) pdf.



MHC2020 - Mixtures, Hidden Markov Models and Clustering Workshop, Institut de mathematique d’Orsay, France (06-21)
Data Science and Finance conference, Siem Reap, Cambodia (03-19)
Masterclass in Bayesian statistics, CIRM, Marseille, France (10-18)
Advances in Finite Mixture and Other Non-regular Models, Guilin, Guangxi, China (08-18)
International conference on Monte Carlo techniques, Paris, France (07-16)
NIPS Workshop Advances in Variational Inference, Montreal, Canada (12-14)
Rencontres statistiques de Rochebrune, Rochebrune, France (04-14)

Other presentations
Seminaire de statistiques de l’ENSAI, Rennes, France, (03-19)
2018 IEEE Data Science Workshop, Lausanne, Switzerland, (06-18)
Seminaire de statistiques du CMLA, ENS Paris-Saclay, France (05-18)
Rencontres statistiques de Rochebrune, Rochebrune, France (03-18)
European Meeting of Statisticians (EMS), Helsinki, Finland (07-17)
Scalable statistical inference, Isaac Newton Institute, Cambridge, UK (07-17)
49emes Journees de Statistique (JdS), Avignon, France (06-17)
Seminaire parisien de statistiques, Paris, France (04-16)
Seminaire de statistiques du laboratoire Jean Kuntzmann, Grenoble, France (04-16)
Sequential Monte Carlo workshop (SMC2015), Paris, France (08-15)
European Meeting of Statisticians (EMS), Amsterdam, Netherlands (07-15)
Young Statisticians and Probabilists seminar, IHP, Paris, France (09-14)
SuSTaIn Workshop High-dimensional Stochastic Simulation and Optimisation, Bristol, UK (08-14)
Seminaire parisien de statistiques, Paris, France (10-13)
Seminaire Methodes de Monte Carlo en Grande Dimension (BigMC), Paris, France (10-13)
36th Conference on Stochastic Processes and Their Applications, Boulder, Colorado (08-13)
5th Greek Stochastics (Jump processes), Kalamata, Greece (07-13)
38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada (05-13)
Seminaire de statistiques AgroParisTech, Paris, France (03-13)
Algorithms and Computationally Intensive Inference Seminar, Warwick, UK (11-12)
Statistics, Applied Probability and Operational Research Seminar, Oxford, UK (11-12)
Recent Advances in Sequential Monte Carlo, Warwick, UK (09-12)
8th World Congress in Probability and Statistics, Istanbul, Turkey (07-12)
10eme Colloque Jeunes Probabilistes et Statisticiens, Marseille, France (04-12)
Seminaire Methodes de Monte Carlo en Grande Dimension (BigMC), Paris, France (12-11)
Statistical Machine Learning in Paris (SMILE), Paris, France (10-10)
28th European Meeting of Statisticians (EMS), Athens, Greece (08-10)