Some invited talks and tutorials
- Arenberg Symposium, Nov. 2019, Artificial Intelligence, News and Questions
- JST CREST Symposium, Sept. 2019, AI and Causal Modeling
- Oberwolfach, May 2019, Structure Agnostic Causal Modeling: SAM
Modeling Relationships Among
Quality of Life at Work and Economic Performances
- Deep Learning Summer School, Genova June 2018, Tutorial on Representation Learning, Domain Adaptation and Generative Models with Deep Learning
- Societe Francaise de Statistique, Jan. 2016, Tutorial on Deep Learning
- AutoML, Aug. 2015, Algorithm Recommendation as Collaborative Filtering
- Constructive Machine Learning, Aug. 2015, Learning from the Human in the Loop
- Intelligence Artificielle et Interfaces Hommes-Machines, 2015,
Programming: The specification era, the machine learning era, the interaction era
- Dagstuhl seminar on Preference learning, Sept. 2014, Preferences, Invariances, Optimization
- At Constraint Programming, Oct. 2012, Tutorial on Monte-Carlo Tree Search
- NeuroComp, Nov. 2011, Tutorial on Validation in Machine Learning
CVs and Previous Activities
On-going Projects
Responsibilities
International
France
- Member of DataIA Board (since 2016)
- Académie des Technologies, Member of Comité des Travaux
- Member of INS2I Council (2010-2014)
On-going PhD students
- Armand Lacombe
- Eléonore Bartenlian (with Frédéric Pascal)
- Victor Berger
- Roman Bresson (with Johanne Cohen)
- Héri Rakotoarison (with Marc Schoenauer)
- Nilo Schwencke (with Etienne Ollion)
Former PhD students
Courses
See Tao Courses
Short Bio
With a background in maths (Ecole Normale Supérieure),
Michèle Sebag went to industry (Thalès) where she started to learn about computer science,
project management, and artificial intelligence.
She got interested in AI, became consulting engineer, and realized that machine learning was something to be. She was offered the opportunity to start research on machine learning for applications in numerical engineering
at Laboratoire de Mécanique des Solides at Ecole Polytechnique.
After her PhD at the crossroad of machine learning
(LRI, Université Paris-Sud Orsay),
data analysis
(Ceremade, Université Paris-10 Dauphine)
and numerical engineering (LMS, Ecole Polytechnique), she entered
CNRS as research fellow (CR1) in 1991.
In 2001, she took the lead of the Inference and ML group,
now ML & Optimization, at LRI, Université Paris-Sud.
In 2003 she founded together with Marc Schoenauer the TAO (ML & Optimization) INRIA project.
Her research interests include causal modelling, preference learning, isurrogate optimization and machine learning applications to social sciences.
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