François Portier

Associate Professor at ENSAI, member of the CREST

Head of the Master for Smart Data science

Associate editor for the Electronic Journal of Statistics

Associate editor for the journal Computational Statistics & Data Analysis

I did my PhD thesis in Rennes at IRMAR (with Bernard Delyon). I was a PostDoc at UCLouvain from 2013 to 2016 (with Johan Segers and Ingrid van Keilegom). Next I was an Assistant professor at Télécom Paris  till 2021. 

Research topics                                                                                 

Lecture notes

Ordinary least squares. PDF

Monte Carlo methods. PDF

Bootstrap and resampling methods. PDF

Recent talks

SGD with coordinate sampling: Theory and Practice slides (used at Chichi León and Marc Lavielle conference in December 22 and Tubingen ELISE workshop in June 23)

Nearest neighbor process.  slides (used at UCLouvain in April 22 and Vienna in October 22)

Control variates with nearest neighbor. slides (used at the gdr-ISIS workshop on Negative dependence in September 22)

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 

Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling. PDF With Lionel Truquet and Ikko Yamane



ENSAI, Campus de Ker Lann

35170 Rennes

Office: 253


33. Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates PDF. Accepted in ICML24. With Aymeric Dieuleveut, Rémi Leluc, Johan Segers, Aigerim Zhuman

32. Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence. To appear in Bernoulli. With Rémi Leluc, Johan Segers, Aigerim Zhuman. PDF

31. Nearest neighbor empirical processes. To appear in Bernoulli. PDF

30. High-dimensional nonconvex lasso-type M-estimators. Journal of multivariate analysis. 2023. With Jad Beyhum. PDF

29. Sharp error bounds for imbalanced classification: how many examples in the minority class? AISTAT24. With Anass Aghbalou and Anne Sabourin. PDF

28. Tail inverse regression for dimension reduction with extreme response. Bernoulli. 2024. With Anass Aghbalou, Anne Sabourin, Chen Zhou. PDF 

27. Asymptotic Analysis of Conditioned Stochastic Gradient Descent. Transaction on Machine Learning Research. 2023. With Rémi Leluc. PDF

26. Consistent Cross Validation with stable learners. AISTAT23. With Anass Aghbalou, Anne Sabourin. PDF

25. Conditional independence testing via weighted partial copulas and nearest neighbors. Journal of multivariate analysis 2023. With Pascal Bianchi and Kevin Elgui. PDF 

24. Risk bounds when learning infinitely many response functions by ordinary linear regression.  Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques 2023. With Vincent Plassier and Johan Segers. PDF

23. SGD with Coordinate Sampling: Theory and Practice. Journal of Machine Learning Research 2022. With Rémi Leluc. PDF  

22. A Quadrature Rule combining Control Variates and Adaptive Importance Sampling.  NeurIPS22. With Rémi Leluc, Johan Segers, Aigerim Zhuman. PDF

21. Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff. AISTAT22 (oral presentation). With Anna Korba. PDF 

20. Empirical risk minimization under random censorship: theory and practice.  Journal of Machine Learning Research 2022. With Guillaime Ausset and Stéphan Clémençon. PDF 

19. On an extension of the promotion time cure model. Annals of statistics 2022. With Jad Beyhum, Anouar El Ghouch and Ingrid Van Keilegom. PDF

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

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

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

15. Nearest neighbour based estimates of gradients: sharp nonasymptotic bounds and applications. AISTAT 2021. With Guillaume Ausset and Stéphan Clémençon. PDF

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

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

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

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

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

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

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

7. On the weak convergence of the empirical conditional copula under a simplifying assumption. Journal of multivariate analysis. 2018. With Johan Segers. PDF

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

5. On the asymptotics of Z-estimators indexed by the objective functions. Electronic journal of statistics. 2016. PDF

4. An empirical process view of inverse regression. Scandinavian journal of statistics. 2016. PDF

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

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

1. Optimal function: A new approach for covering the central subspace. Journal of multivariate analysis. 2013. With Bernard Delyon. PDF

PhD students

Victor Priser. With Pascal Bianchi. 

Anass Aghbalou. With Anne Sabourin. Defended in Feb 2024.

Rémi Leluc. With Pascal Bianchi. Defended in March 2023.

Guillaume Ausset. With Stéphan Clémençon. Defended in November 2021.

Kamelia Daudel. With Randal Douc and François Roueff. Defended in September 2021.

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.