Weighted algorithms for compressed sensing and matrix completion - Stéphane Gaïffas; Guillaume Lecué (CoRR, 2011)

Hyper-Sparse Optimal Aggregation. - Stéphane Gaïffas; Guillaume Lecué (J. Mach. Learn. Res., 2011)

Sharp Oracle Inequalities for High-Dimensional Matrix Prediction. - Stéphane Gaïffas; Guillaume Lecué (IEEE Trans. Inf. Theory, 2011)

Learning to Target the Target: Automatic Audience Segmentation. - Carlos Sureda Gutierrez; Elodie Prodhomme; Stéphane Gaïffas (ICDM (Workshops), 2013)

Link prediction in graphs with autoregressive features. - Emile Richard; Stéphane Gaïffas; Nicolas Vayatis (J. Mach. Learn. Res., 2014)

SGD with Variance Reduction beyond Empirical Risk Minimization. - Massil Achab; Agathe Guilloux; Stéphane Gaïffas; Emmanuel Bacry (CoRR, 2015)

Mean-field inference of Hawkes point processes. - Emmanuel Bacry; Stéphane Gaïffas; Iacopo Mastromatteo; Jean-François Muzy (CoRR, 2015)

Learning the Intensity of Time Events With Change-Points. - Mokhtar Z. Alaya; Stéphane Gaïffas; Agathe Guilloux (IEEE Trans. Inf. Theory, 2015)

Universal consistency and minimax rates for online Mondrian Forests. - Jaouad Mourtada; Stéphane Gaïffas; Erwan Scornet (NIPS, 2017)

Uncovering Causality from Multivariate Hawkes Integrated Cumulants. - Massil Achab; Emmanuel Bacry; Stéphane Gaïffas; Iacopo Mastromatteo; Jean-François Muzy (J. Mach. Learn. Res., 2017)

tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models. - Emmanuel Bacry; Martin Bompaire; Philip Deegan; Stéphane Gaïffas; Søren Poulsen (J. Mach. Learn. Res., 2017)

Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption. - Martin Bompaire; Emmanuel Bacry; Stéphane Gaïffas (CoRR, 2018)

Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework. - Simon Bussy; Raphaël Veil; Vincent Looten; Anita Burgun; Stéphane Gaïffas; Agathe Guilloux; Brigitte Ranque; Anne-Sophie Jannot (CoRR, 2018)

Anytime Hedge achieves optimal regret in the stochastic regime. - Jaouad Mourtada; Stéphane Gaïffas (CoRR, 2018)

AMF: Aggregated Mondrian Forests for Online Learning. - Jaouad Mourtada; Stéphane Gaïffas; Erwan Scornet (CoRR, 2019)

ZiMM: a deep learning model for long term adverse events with non-clinical claims data. - Emmanuel Bacry; Stéphane Gaïffas; Anastasiia Kabeshova; Yiyang Yu (CoRR, 2019)

Binarsity: a penalization for one-hot encoded features in linear supervised learning. - Mokhtar Z. Alaya; Simon Bussy; Stéphane Gaïffas; Agathe Guilloux (J. Mach. Learn. Res., 2019)

On the optimality of the Hedge algorithm in the stochastic regime. - Jaouad Mourtada; Stéphane Gaïffas (J. Mach. Learn. Res., 2019)

Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck process. - Stéphane Gaïffas; Gustaw Matulewicz (J. Multivar. Anal., 2019)

About contrastive unsupervised representation learning for classification and its convergence. - Ibrahim Merad; Yiyang Yu; Emmanuel Bacry; Stéphane Gaïffas (CoRR, 2020)

SCALPEL3: A scalable open-source library for healthcare claims databases. - Emmanuel Bacry; Stéphane Gaïffas; Fanny Leroy; Maryan Morel; Dinh Phong Nguyen; Youcef Sebiat; Dian Sun (Int. J. Medical Informatics, 2020)

ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data. - Anastasiia Kabeshova; Yiyang Yu; Bertrand Lukacs; Emmanuel Bacry; Stéphane Gaïffas (J. Biomed. Informatics, 2020)

Sparse and low-rank multivariate Hawkes processes. - Emmanuel Bacry; Martin Bompaire; Stéphane Gaïffas; Jean-François Muzy (J. Mach. Learn. Res., 2020)

A Review on Contrastive Learning Methods and Applications to Roof-Type Classification on Aerial Images. - Ahmed Ben Saad; Sébastien Drouyer; Bastien Hell; Sylvain Gavoille; Stéphane Gaïffas; Gabriele Facciolo (IGARSS, 2021)

Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data. - Daniel de Barros Soares; François Andrieux; Bastien Hell; Julien Lenhardt; Jordi Badosa; Sylvain Gavoille; Stéphane Gaïffas; Emmanuel Bacry (CoRR, 2021)

An improper estimator with optimal excess risk in misspecified density estimation and logistic regression. - Jaouad Mourtada; Stéphane Gaïffas (J. Mach. Learn. Res., 2022)

Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization. - Massil Hihat; Stéphane Gaïffas; Guillaume Garrigos; Simon Bussy (NeurIPS, 2023)

Convergence and concentration properties of constant step-size SGD through Markov chains. - Ibrahim Merad; Stéphane Gaïffas (CoRR, 2023)

Robust Methods for High-Dimensional Linear Learning. - Ibrahim Merad; Stéphane Gaïffas (J. Mach. Learn. Res., 2023)

Robust supervised learning with coordinate gradient descent. - Ibrahim Merad; Stéphane Gaïffas (Stat. Comput., 2023)

WildWood: A New Random Forest Algorithm. - Stéphane Gaïffas; Ibrahim Merad; Yiyang Yu (IEEE Trans. Inf. Theory, 2023)

Robust Stochastic Optimization via Gradient Quantile Clipping. - Ibrahim Merad; Stéphane Gaïffas (Trans. Mach. Learn. Res., 2024)