SequeL team

Sequential Learning

Leader: Philippe Preux



SequeL is a research group working in the field of machine learning; more specifically, SequeL is dedicated to the study of the problem of sequential decision making under uncertainty, that is, the study of how an “agent” having a goal to fullfill can learn an optimal behavior to achieve this goal in an unknown environment. SequeL is composed of two dozens members. Activities range from foundations of learning to algorithm design, and transfer towards companies. Questions are studied such as “What can a Turing machine learn efficiently? and in which conditions?”. Or, in a budget context, “Given an amount of computational resource, how close to the optimal behavior can an algorithm reach?”, finally application oriented questions such as those related to computational advertizing and recommendation systems for e-commerce websites, are also studied.

SequeL has led to the multi-awarded Crazy Stone go playing program. Some SequeL PhD students have been awarded the Gilles Kahn award, the Jacques Neveu award and the ECCAI award. We won the ICML 2011 Exploration vs. Exploitation challenge, and the ACM RecSYS 2014 challenge (both challenges on recommendation systems). SequeL expertize has led to collaborations with international companies like Orange Labs, Intel, Technicolor, Deezer and also with national and local SMEs.


Dorian Baudry

Efficient Exploration in Structured Bandits and Reinforcement Learning

Omar Darwiche Domingues

Sequential Learning in Dynamic Environments

Johan Ferret

Apprentissage par renforcement explicable

Yannis Flet-Berliac

Deep Reinforcement Learning in Stochastic and non Stationary Environments

Nathan Grinsztajn

Apprentissage par renforcement pour la résolution séquentielle de problèmes d'optimisation combinatoire incertains et partiellement définis

Léonard Hussenot Desenonges

Apprentissage par renforcement et robustesse face aux attaques adversariales

Edouard Leurent

Conduite automobile autonome : application des techniques d'apprentissage automatique à la planification contextualisée de trajectoire

Reda Ouhamma

Bandits non stationnaires et recommandations médicales

Pierre Perrault

Online Learning on Streaming Graphs

Sarah Perrin

Apprentissage par renforcement dans les jeux à champ moyen

Hassan Saber

Structure adaptation in reinforcement learning

Mathieu Seurin

Problème de récompenses Multi'échelle dans le contexte de l'apprentissage par renforcement

Julien Seznec

Sequential Learning for Educationnal System

Xuedong Shang

Méthodes adaptatives pour l'optimisation dans un environnement stochastique

Jean Tarbouriech

Exploration/exploitation à grande échelle

Kiewan Villatel

Sequential Learning for Online Advertising

Les autres équipes du groupe thématique ' DatInG '