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
News
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
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
francois.portier_gmail_com
Papers
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,
more about the wind in Bretagne.