1 code implementation • 23 Feb 2023 • Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
no code implementations • 5 Oct 2022 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
The performance gap is reduced with bigger datasets, but considering the increased computational load, end-to-end training may not be profitable for this application.
no code implementations • 8 Sep 2022 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.
no code implementations • 19 Jun 2022 • Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati
In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply closely correlated losses that may confound standard discrete choice models and bandit algorithms.
1 code implementation • 11 Feb 2022 • Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
no code implementations • 5 Oct 2021 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
These models have a limited representational capacity and may fail to capture the relationship between ECoG signal and continuous hand movements.
no code implementations • 13 Sep 2021 • Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier
We propose a hierarchical version of dual averaging for zeroth-order online non-convex optimization - i. e., learning processes where, at each stage, the optimizer is facing an unknown non-convex loss function and only receives the incurred loss as feedback.
no code implementations • 24 Feb 2021 • Matthieu Martin, Thomas Risler
We describe a viscocapillary instability that can perturb the spherical symmetry of cellular aggregates in culture, also called multicellular spheroids.
Biological Physics Soft Condensed Matter Medical Physics Tissues and Organs
no code implementations • 5 Dec 2020 • Matthieu Martin, Bruno Sciolla, Michaël Sdika, Philippe Quétin, Philippe Delachartre
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS).
no code implementations • NeurIPS 2020 • Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier
We consider the problem of online learning with non-convex losses.
no code implementations • 7 Aug 2020 • Thibaud Rahier, Amélie Héliou, Matthieu Martin, Christophe Renaudin, Eustache Diemert
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains.
1 code implementation • 22 Apr 2020 • Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.