- HAL publications
CELESTE Research team
mathematical statistics and learning
- Leader : Sylvain Arlot
- Type : Project team
- Research center(s) : Saclay
- Field : Applied Mathematics, Computation and Simulation
- Theme : Optimization, machine learning and statistical methods
- Partner(s) : CNRS,Université Paris-Sud (Paris 11)
- Collaborator(s) : U. PARIS 11 (P.-SUD)
The positioning of the CELESTE project-team is at the interplay between statistics and learning. We are statisticians, members of a mathematics laboratory, with a strong mathematical background, and are interested by interactions between theory, algorithms and applications. Indeed, applications lead to most interesting theoretical problems, while theory can play a key role in (i) understanding how and why successful statistical/learning algorithms work — hence improving them — and (ii) building new algorithms upon mathematical statistics foundations.
Our work involves analyzing popular statistical learning algorithms from a mathematical statistics point of view and developing new learning algorithms based upon our skill set. Our main methodological and theoretical research axes are:
- estimator selection
- the relationship between statistical accuracy and computational complexity
- robustness to outliers and heavy tails
- statistical inference: (multiple) tests and confidence regions.
A key ingredient in our research program is matching our theoretical/methodological results with numerous real-world situations. Indeed, CELESTE members work in many domains including – but not limited to – neglected tropical diseases, pharmacovigilance, high-dimensional transcriptomic analysis, and energy and the environment.
International and industrial relations
Celeste has several ongoing collaborations with the R&D department of EDF, and collaborates with a number of other companies (via CIFRE Ph.D. theses for instance).
Celeste has academic collaborations with researchers from many institutions around the world, including MPI Tubingen, University of Warwick, Cornell University, Brown University, University of Washington at Seattle, Princeton University and IMPA Rio.
Research teams of the same theme :
- BONUS - Big Optimization aNd Ultra-Scale Computing
- 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
- SEQUEL - Sequential Learning
- SIERRA - Statistical Machine Learning and Parsimony
- TAU - Tackling the under-specified