Optimal transformation: A new approach for covering the central subspace. - François Portier; Bernard Delyon (J. Multivar. Anal., 2013)

Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. - Stéphan Clémençon; François Portier (AISTATS, 2018)

Asymptotic optimality of adaptive importance sampling. - François Portier; Bernard Delyon (NeurIPS, 2018)

On the weak convergence of the empirical conditional copula under a simplifying assumption. - François Portier; Johan Segers (J. Multivar. Anal., 2018)

Empirical Risk Minimization under Random Censorship: Theory and Practice. - Guillaume Ausset; Stéphan Clémençon; François Portier (CoRR, 2019)

Monte Carlo integration with a growing number of control variates. - François Portier; Johan Segers (J. Appl. Probab., 2019)

Metric Learning for Fingerprint RSSI-Localization. - Kevin Elgui; Pascal Bianchi; Olivier Isson; François Portier; Renaud Marty (PLANS, 2020)

Towards Asymptotic Optimality with Conditioned Stochastic Gradient Descent. - Rémi Leluc; François Portier (CoRR, 2020)

Risk bounds when learning infinitely many response functions by ordinary linear regression. - Vincent Plassier; François Portier; Johan Segers (CoRR, 2020)

Learning methods for RSSI-based geolocation: A comparative study. - Kevin Elgui; Pascal Bianchi; François Portier; Olivier Isson (Pervasive Mob. Comput., 2020)

Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. - Guillaume Ausset; Stéphan Clémençon; François Portier (AISTATS, 2021)

Individual Survival Curves with Conditional Normalizing Flows. - Guillaume Ausset; Tom Ciffreo; François Portier; Stéphan Clémençon; Timothée Papin (DSAA, 2021)

High dimensional regression for regenerative time-series: An application to road traffic modeling. - Mohammed Lamine Bouchouia; François Portier (Comput. Stat. Data Anal., 2021)

Control variate selection for Monte Carlo integration. - Rémi Leluc; François Portier; Johan Segers (Stat. Comput., 2021)

Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff. - Anna Korba; François Portier (AISTATS, 2022)

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling. - Rémi Leluc; François Portier; Johan Segers; Aigerim Zhuman (NeurIPS, 2022)

Empirical Risk Minimization under Random Censorship. - Guillaume Ausset; Stéphan Clémençon; François Portier (J. Mach. Learn. Res., 2022)

SGD with Coordinate Sampling: Theory and Practice. - Rémi Leluc; François Portier (J. Mach. Learn. Res., 2022)

On the bias of K-fold cross validation with stable learners. - Anass Aghbalou; Anne Sabourin; François Portier (AISTATS, 2023)

Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence. - Rémi Leluc; François Portier; Johan Segers; Aigerim Zhuman (CoRR, 2023)

Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling. - François Portier; Lionel Truquet; Ikko Yamane (CoRR, 2023)

Conditional independence testing via weighted partial copulas. - Pascal Bianchi; Kevin Elgui; François Portier (J. Multivar. Anal., 2023)

Asymptotic Analysis of Conditioned Stochastic Gradient Descent. - Rémi Leluc; François Portier (Trans. Mach. Learn. Res., 2023)

Sharp error bounds for imbalanced classification: how many examples in the minority class? - Anass Aghbalou; Anne Sabourin; François Portier (AISTATS, 2024)

Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. - Rémi Leluc; Aymeric Dieuleveut; François Portier; Johan Segers; Aigerim Zhuman (ICML, 2024)

High-dimensional nonconvex LASSO-type M-estimators. - Jad Beyhum; François Portier (J. Multivar. Anal., 2024)