Stéphane Gaïffas

DeepAI AI Chat

Featured Co-authors

Nicolas Vayatis
34 publications
Erwan Scornet
23 publications
Malgorzata Bogdan
15 publications
Jaouad Mourtada
12 publications
Guillaume Lecué
12 publications
Agathe Guilloux
12 publications
Bertrand Michel
10 publications
Emmanuel Bacry
10 publications
Emile Richard
6 publications
Martin Bompaire
5 publications
Mokhtar Z. Alaya
4 publications

research

∙ 08/10/2022

Robust methods for high-dimensional linear learning

We propose statistically robust and computationally efficient linear lea...

0 Ibrahim Merad, et al. ∙

research

∙ 01/31/2022

Robust supervised learning with coordinate gradient descent

This paper considers the problem of supervised learning with linear meth...

0 Stéphane Gaïffas, et al. ∙

research

∙ 09/16/2021

WildWood: a new Random Forest algorithm

We introduce WildWood (WW), a new ensemble algorithm for supervised lear...

0 Stéphane Gaïffas, et al. ∙

research

∙ 12/02/2020

About contrastive unsupervised representation learning for classification and its convergence

Contrastive representation learning has been recently proved to be very ...

0 Ibrahim Merad, et al. ∙

research

∙ 12/23/2019

An improper estimator with optimal excess risk in misspecified density estimation and logistic regression

We introduce a procedure for predictive conditional density estimation u...

8 Jaouad Mourtada, et al. ∙

research

∙ 11/13/2019

ZiMM: a deep learning model for long term adverse events with non-clinical claims data

This paper considers the problem of modeling long-term adverse events fo...

19 Emmanuel Bacry, et al. ∙

research

∙ 10/15/2019

SCALPEL3: a scalable open-source library for healthcare claims databases

This article introduces SCALPEL3, a scalable open-source framework for s...

0 Emmanuel Bacry, et al. ∙

research

∙ 06/25/2019

AMF: Aggregated Mondrian Forests for Online Learning

Random Forests (RF) is one of the algorithms of choice in many supervise...

0 Jaouad Mourtada, et al. ∙

research

∙ 09/05/2018

Anytime Hedge achieves optimal regret in the stochastic regime

This paper is about a surprising fact: we prove that the anytime Hedge a...

2 Jaouad Mourtada, et al. ∙

research

∙ 07/25/2018

Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework

Background: Choosing the most performing method in terms of outcome pred...

4 Simon Bussy, et al. ∙

research

∙ 07/10/2018

Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption

The minimization of convex objectives coming from linear supervised lear...

6 Martin Bompaire, et al. ∙

research

∙ 03/15/2018

Minimax optimal rates for Mondrian trees and forests

Introduced by Breiman (2001), Random Forests are widely used as classifi...

0 Jaouad Mourtada, et al. ∙

research

∙ 12/21/2017

ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection

With the increased availability of large databases of electronic health ...

0 Maryan Morel, et al. ∙

research

∙ 12/07/2017

High-dimensional robust regression and outliers detection with SLOPE

The problems of outliers detection and robust regression in a high-dimen...

0 Alain Virouleau, et al. ∙

research

∙ 11/08/2017

Universal consistency and minimax rates for online Mondrian Forests

We establish the consistency of an algorithm of Mondrian Forests, a rand...

0 Jaouad Mourtada, et al. ∙

research

∙ 07/10/2017

Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck process

Given the observation of a high-dimensional Ornstein-Uhlenbeck (OU) proc...

0 Stéphane Gaïffas, et al. ∙

research

∙ 07/10/2017

tick: a Python library for statistical learning, with a particular emphasis on time-dependent modeling

tick is a statistical learning library for Python 3, with a particular e...

0 Emmanuel Bacry, et al. ∙

research

∙ 03/24/2017

Binarsity: a penalization for one-hot encoded features

This paper deals with the problem of large-scale linear supervised learn...

0 Mokhtar Z. Alaya, et al. ∙

research

∙ 10/24/2016

C-mix: a high dimensional mixture model for censored durations, with applications to genetic data

We introduce a mixture model for censored durations (C-mix), and develop...

0 Simon Bussy, et al. ∙

research

∙ 07/21/2016

Uncovering Causality from Multivariate Hawkes Integrated Cumulants

We design a new nonparametric method that allows one to estimate the mat...

0 Massil Achab, et al. ∙

research

∙ 10/16/2015

SGD with Variance Reduction beyond Empirical Risk Minimization

We introduce a doubly stochastic proximal gradient algorithm for optimiz...

0 Massil Achab, et al. ∙

research

∙ 07/02/2015

Learning the intensity of time events with change-points

We consider the problem of learning the inhomogeneous intensity of a cou...

0 Mokhtar Zahdi Alaya, et al. ∙

research

∙ 01/04/2015

A generalization error bound for sparse and low-rank multivariate Hawkes processes

We consider the problem of unveiling the implicit network structure of u...

0 Emmanuel Bacry, et al. ∙

research

∙ 12/24/2014

Concentration for matrix martingales in continuous time and microscopic activity of social networks

This paper gives new concentration inequalities for the spectral norm of...

0 Emmanuel Bacry, et al. ∙

research

∙ 01/30/2014

Sparse Bayesian Unsupervised Learning

This paper is about variable selection, clustering and estimation in an ...

0 Stéphane Gaïffas, et al. ∙

research

∙ 09/14/2012

Link Prediction in Graphs with Autoregressive Features

In the paper, we consider the problem of link prediction in time-evolvin...

0 Emile Richard, et al. ∙

research

∙ 12/08/2009

Hyper-sparse optimal aggregation

In this paper, we consider the problem of "hyper-sparse aggregation". Na...

0 Stéphane Gaïffas, et al. ∙