Le Séminaire Palaisien

« Le Séminaire Palaisien » | Solenne Gaucher et Nicolas Vayatis sur l'apprentissage automatique et la statistique

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Le séminaire Palaisien réunit, chaque premier mardi du mois, la vaste communauté de recherche de Saclay autour de la statistique et de l'apprentissage automatique.
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Chaque session du séminaire est divisée en deux présentations scientifiques de 40 minutes chacune : 30 minutes d’exposé et 10 minutes de questions.

Solenne Gaucher (Université Paris-Saclay) et Nicolas Vayatis (ENS Paris-Saclay) animeront la session de mars 2021.

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« Continuum-armed bandits: from the classical setting to the finite setting » - Solenne Gaucher
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Bandits are used to model the following sequential decision problem : at each time step, an agent takes an action and receives a reward drawn i.i.d. from a distribution depending on the action she has selected. Her aim is to maximize her cumulative reward. In this talk, we start by introducing the continuum-armed bandit, and present classical algorithms and results. In this setting, actions are indexed by covariates in a continuous set. The expected reward for taking an action is modeled as an (unknown) function of the covariate describing this action. In a second time, we focus on the finite continuum-armed bandit setting, where the set of actions is finite, and each action can only be taken once.

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« Can Machine Learning predict Human Behavior? » - Nicolas Vayatis
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Behavioral neurosciences are about to undergo a major shift due to a) the dissemination of wearable and ambient sensors in routine clinical assessment or in complex Human-Machine interaction, b) the progressive abandonment of animal experimentation. This raises several issues in terms of observational data, but also questions how Machine Learning techniques may contribute to the data-driven quantification and prediction of Human behavior. In this talk, I will introduce the topic of Machine Learning applications to behavioral neurosciences. I will also give an overview of recent works and questions for the future of Machine Learning research in this field.