François Portier

Associate Professor at ENSAI, member of the CREST

Associate editor for the journal Computational Statistics & Data Analysis

Research topics

  • Monte Carlo methods

  • Semiparametric statistics


  • I organize a session on Monte Carlo methods at the next ISNPS conference (which has been rescheduled to 2022)

Lecture notes

Ordinary least squares. PDF

Monte Carlo methods. PDF

Bootstrap and resampling methods. PDF

Recent talks

Asymptotic Optimality with Conditioned SGD. slides (used at LAREMA in april 2021)

A guided tour in Monte Carlo. slides (used at KUL in march 2019)

New Papers

Nearest neighbor process: weak convergence and non-asymptotic bound. PDF

Towards Asymptotic Optimality with Conditioned Stochastic Gradient Descent. With Rémi Leluc. PDF

SGD with Coordinate Sampling: Theory and Practice. With Rémi Leluc. PDF

Risk bounds when learning infinitely many response functions by ordinary linear regression. With Vincent Plassier and Johan Segers . PDF

Empirical risk minimization under random censorship: theory and practice. With Guillaime Ausset and Stéphan Clémençon. PDF

ENSAI, Campus de Ker Lann

35170 Rennes

Office: 253



On an extension of the promotion time cure model. To appear in Annals of statistics. With Jad Beyhum, Anouar El Ghouch and Ingrid Van Keilegom. PDF

Infinite-dimensional gradient-based descent for alpha-divergence minimisation. Annals of statistics 2021. With Kamelia Daudel and Randal Douc. PDF

Control variate selection for Monte Carlo integration. Statistics and computing 2021. With Rémi Leluc and Johan Segers. PDF

Safe and adaptive importance sampling: a mixture approach. Annals of statistics 2021. With Bernard Delyon. PDF

Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. AISTAT 2021. With Guillaume Ausset and Stéphan Clémençon.

High dimensional regression for regenerative time-series: an application to road traffic modeling. Computational statistics and data analysis. 2021. With Mohammed Bouchouia. PDF

Parametric versus nonparametric : the fitness coefficient. Scandinavian journal of statistics. 2020. With Gildas Mazo. PDF

Monte Carlo integration with a growing number of control variates. Journal of applied probability. 2019. With Johan Segers. PDF

Rademacher complexity for Markov chains : Applications to kernel smoothing and Metropolis-Hastings. Bernoulli. 2019. With Patrice Bertail. PDF

Asymptotic optimality of adaptive importance sampling. NeurIPS. 2018. With Bernard Delyon. PDF

Integral estimation based on Markovian design. Advances in applied probability. 2018. With Romain Azais and Bernard Delyon. PDF

Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. AISTAT. 2018. With Stéphan Clémençon.

On the weak convergence of the empirical conditional copula under a simplifying assumption. Journal of Multivariate Analysis. 2018. With Johan Segers. PDF

Efficiency and bootstrap in the promotion time cure model. Bernoulli. 2017. With Anouar El Ghouch and Ingrid Van Keilegom. PDF

On the asymptotics of Z-estimators indexed by the objective functions. Electronic Journal of Statistics. 2016. PDF

An empirical process view of inverse regression. Scandinavian Journal of Statistics. 2016. PDF

Integral approximation by kernel smoothing. Bernoulli. 2016. With Bernard Delyon. PDF

Bootstrap testing of the rank of a matrix via least squared constrained estimation. Journal of the American Statistical Association. 2014. With Bernard Delyon. PDF

Optimal function: A new approach for covering the central subspace. Journal of Multivariate Analysis. 2013. With Bernard Delyon. PDF

PhD students

Rémi Leluc. With Pascal Bianchi.

Kamelia Daudel. With Randal Douc and François Roueff.

Guillaume Ausset. With Stéphan Clémençon.

Kevin Elgui. With Pascal Bianchi. Defended in December 2020.

Other Links

Arthur Charpentier's blog about statistics, actuarial sciences and R,

Christian Robert's blog about Bayesian statistics and Monte Carlo methods,

MathSciNet, arXiv,

more about the wind in Bretagne.