1 code implementation • 30 Jan 2024 • Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian, Agathe Guilloux
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series.
no code implementations • 26 May 2023 • Linus Bleistein, Agathe Guilloux
Neural Controlled Differential Equations (NCDEs) are a state-of-the-art tool for supervised learning with irregularly sampled time series (Kidger, 2020).
1 code implementation • 27 Jan 2023 • Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux
We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series.
2 code implementations • 29 Mar 2022 • Ayoub Abraich, Agathe Guilloux, Blaise Hanczar
Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.
no code implementations • 25 Jul 2018 • Simon Bussy, Raphaël Veil, Vincent Looten, Anita Burgun, Stéphane Gaïffas, Agathe Guilloux, Brigitte Ranque, Anne-Sophie Jannot
We then compare performances of all methods both in terms of risk prediction and variable selection, with a focus on the use of Elastic-Net regularization technique.
1 code implementation • 25 Jul 2018 • Simon Bussy, Mokhtar Z. Alaya, Anne-Sophie Jannot, Agathe Guilloux
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available.
1 code implementation • 21 Dec 2017 • Maryan Morel, Emmanuel Bacry, Stéphane Gaïffas, Agathe Guilloux, Fanny Leroy
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening.
no code implementations • 7 Dec 2017 • Alain Virouleau, Agathe Guilloux, Stéphane Gaïffas, Malgorzata Bogdan
Following a recent set of works providing methods for simultaneous robust regression and outliers detection, we consider in this paper a model of linear regression with individual intercepts, in a high-dimensional setting.
no code implementations • 24 Mar 2017 • Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas, Agathe Guilloux
In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint.
1 code implementation • 24 Oct 2016 • Simon Bussy, Agathe Guilloux, Stéphane Gaïffas, Anne-Sophie Jannot
We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model.
no code implementations • 16 Oct 2015 • Massil Achab, Agathe Guilloux, Stéphane Gaïffas, Emmanuel Bacry
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations.
no code implementations • 2 Jul 2015 • Mokhtar Zahdi Alaya, Stéphane Gaïffas, Agathe Guilloux
We prove that this leads to a sharp tuning of the convex relaxation of the segmentation prior, by stating oracle inequalities with fast rates of convergence, and consistency for change-points detection.