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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)



Field: Applied Mathematics, Computation and Simulation

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