45 citations · 109 across the 11 of their papers we have counts for
13 papers
Minimax optimal rates for Mondrian trees and forests
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
Introduced by Breiman, Random Forests are widely used classification and regression algorithms. While being initially designed as batch algorithms, several variants have been propo…
High-dimensional robust regression and outliers detection with SLOPE
Alain Virouleau, Agathe Guilloux, Stéphane Gaïffas +1
The problems of outliers detection and robust regression in a high-dimensional setting are fundamental in statistics, and have numerous applications. Following a recent set of work…
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas +1
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survi…
Binarsity: a penalization for one-hot encoded features in linear supervised learning
Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas +1
This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-kn…
Sparse Bayesian Unsupervised Learning
Stephane Gaiffas, Bertrand Michel
This paper is about variable selection, clustering and estimation in an unsupervised high-dimensional setting. Our approach is based on fitting constrained Gaussian mixture models,…
High dimensional matrix estimation with unknown variance of the noise
Olga Klopp, Stéphane Gaiffas
We propose a new pivotal method for estimating high-dimensional matrices. Assume that we observe a small set of entries or linear combinations of entries of an unknown matrix $A\_0…