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2019 Project-Team Activity Report
CELESTE
mathematical statistics and learning
Research centre:
Saclay - Île-de-France
In partnership with: CNRS, Université Paris-Sud (Paris 11)
In collaboration with: Laboratoire de mathématiques d'Orsay de l'Université de Paris-Sud (LMO)
In collaboration with: Laboratoire de mathématiques d'Orsay de l'Université de Paris-Sud (LMO)
Field: Applied Mathematics, Computation and Simulation
Theme: Optimization, machine learning and statistical methods
Theme: Optimization, machine learning and statistical methods
Keywords:
Computer Science and Digital Science:
- A3.1.1. - Modeling, representation
- A3.1.8. - Big data (production, storage, transfer)
- A3.3. - Data and knowledge analysis
- A3.3.3. - Big data analysis
- A3.4. - Machine learning and statistics
- A3.4.1. - Supervised learning
- A3.4.2. - Unsupervised learning
- A3.4.3. - Reinforcement learning
- A3.4.4. - Optimization and learning
- A3.4.5. - Bayesian methods
- A3.4.7. - Kernel methods
- A3.5.1. - Analysis of large graphs
- A5.9.2. - Estimation, modeling
- A6. - Modeling, simulation and control
- A6.1. - Methods in mathematical modeling
- A6.2. - Scientific computing, Numerical Analysis & Optimization
- A6.2.4. - Statistical methods
- A6.3. - Computation-data interaction
- A6.3.1. - Inverse problems
- A6.3.3. - Data processing
- A6.3.4. - Model reduction
- A9.2. - Machine learning
Other Research Topics and Application Domains:
- B1.1.4. - Genetics and genomics
- B1.1.7. - Bioinformatics
- B2.2.4. - Infectious diseases, Virology
- B2.3. - Epidemiology
- B2.4.1. - Pharmaco kinetics and dynamics
- B3.4. - Risks
- B4. - Energy
- B4.4. - Energy delivery
- B4.5. - Energy consumption
- B5.2.1. - Road vehicles
- B5.2.2. - Railway
- B5.2.3. - Aviation
- B5.5. - Materials
- B5.9. - Industrial maintenance
- B7.1. - Traffic management
- B7.1.1. - Pedestrian traffic and crowds
- B9.5.2. - Mathematics
- B9.8. - Reproducibility