Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Martin Matthieu"'
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. In this paper, we explore the case where it is possible to deploy learned pol
Externí odkaz:
http://arxiv.org/abs/2302.12120
Autor:
Śliwowski, Maciej, Martin, Matthieu, Souloumiac, Antoine, Blanchart, Pierre, Aksenova, Tetiana
In brain signal processing, deep learning (DL) models have become commonly used. However, the performance gain from using end-to-end DL models compared to conventional ML approaches is usually significant but moderate, typically at the cost of increa
Externí odkaz:
http://arxiv.org/abs/2210.02544
Autor:
Śliwowski, Maciej, Martin, Matthieu, Souloumiac, Antoine, Blanchart, Pierre, Aksenova, Tetiana
In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size.
Externí odkaz:
http://arxiv.org/abs/2209.03789
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
Externí odkaz:
http://arxiv.org/abs/2206.09348
Autor:
Zenati, Houssam, Bietti, Alberto, Diemert, Eustache, Mairal, Julien, Martin, Matthieu, Gaillard, Pierre
In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. 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
Externí odkaz:
http://arxiv.org/abs/2202.05638
Autor:
Śliwowski, Maciej, Martin, Matthieu, Souloumiac, Antoine, Blanchart, Pierre, Aksenova, Tetiana
Publikováno v:
Journal of Neural Engineering 19, no. 2 (March 31, 2022): 026023
Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients
Externí odkaz:
http://arxiv.org/abs/2110.03528
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 fe
Externí odkaz:
http://arxiv.org/abs/2109.05829
Autor:
Renaudin, Christophe, Martin, Matthieu
In this tech report we discuss the evaluation problem of contextual uplift modeling from the causal inference point of view. More particularly, we instantiate the individual treatment effect (ITE) estimation, and its evaluation counterpart. First, we
Externí odkaz:
http://arxiv.org/abs/2107.00537
Autor:
Martin, Matthieu, Risler, Thomas
Publikováno v:
New J. Phys. 23 033032 (2021)
We describe a viscocapillary instability that can perturb the spherical symmetry of cellular aggregates in culture, also called multicellular spheroids. In the condition where the cells constituting the spheroid get their necessary metabolites from t
Externí odkaz:
http://arxiv.org/abs/2102.12340
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Con
Externí odkaz:
http://arxiv.org/abs/2012.03014