SEQUEL Research team
- Leader : Philippe Preux
- Type : Project team
- Research center(s) : Lille
- Field : Applied Mathematics, Computation and Simulation
- Theme : Optimization, machine learning and statistical methods
- Partner(s) : Université Charles de Gaulle (Lille 3),Université des sciences et technologies de Lille (Lille 1)
- Collaborator(s) : Centre de Recherche en Informatique, Signal et Automatique de Lille (9189)
Lots of artificial systems (either software agent, or hardware robot) obtain their data sequentially, along time. For instance, these data may be web pages that are created, or modified, or removed; these data may be sensor measures gathered along time, either passively, or actively by a system having a feedback on its enviromnent. From these data, these systems extract information that may be used to detect objects (classification problem), or to estimate the parameters of a process (estimation problem), or to interact with its environment (sequential decision problem). Sometimes, the amount of data is also so huge that the only way to process it is to split the data and process each chunk at a time. In all cases, we expect that at each instant, the system is able to provide an adequate response. Typically, the environment is stochastic, and may be non stationary.
The goal of the project-team is to develop concepts and algorithms that are able to process efficiently, and with a known and controlled accuracy, these tasks of sequential learning.
The applications are potentially very numerous. Currently, we work on trecommendation systems. We have had a very significant contribution to go, and more generally games, with the award winning Crazy Stone software.
- sequential learning
- decision making under uncertainty
- bandit problems; exploration/exploitation dilemma
- reinforcement learning
International and industrial relations
- industrielle : Google, Deepmind, Facebook AI Research, Critéo, Renault, Le Livre Scolaire, ...
- scientifique : U. Mc Gill (Canada), U. Leoben (Austria), LIP 6, MILA (montréal), ...
Research teams of the same theme :
- BONUS - Big Optimization aNd Ultra-Scale Computing
- CELESTE - mathematical statistics and learning
- GEOSTAT - Geometry and Statistics in acquisition data
- INOCS - INtegrated Optimization with Complex Structure
- MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
- MODAL - MOdel for Data Analysis and Learning
- RANDOPT - Randomized Optimization
- REALOPT - Reformulations based algorithms for Combinatorial Optimization
- SELECT-POST - Model selection in statistical learning
- SIERRA - Statistical Machine Learning and Parsimony
- TAU - TAckling the Underspecified