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
Monte Carlo methods
Statistical learning
Semiparametric statistics
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
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates PDF. With Aymeric Dieuleveut, Rémi Leluc, Johan Segers, Aigerim Zhuman
CONTACT
francois.portier_gmail_com
ENSAI, Campus de Ker Lann
35170 Rennes
Office: 253
Papers
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. To appear in Journal of multivariate analysis. With Jad Beyhum. PDF
29. Sharp error bounds for imbalanced classification: how many examples in the minority class? To appear in 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,
more about the wind in Bretagne.