Michèle Sebag

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Michèle Sebag

Michèle Sebag,
a French mathematician and computer scientist, head of Laboratoire de Recherche en Informatique, LRI, French National Centre for Scientific Research, CNRS, Paris-Sud 11 University, and along with Marc Schoenauer head the TAO Project Team , National Institute for Research in Computer Science and Control, INRIA, Saclay, Île-de-France. Her research interests include the broad range of artificial intelligence, machine learning, knowledge discovery, stochastic optimization and genetic programming .

Selected Publications

1990 ...

• Michèle Sebag (1990). A symbolic-numerical approach for supervised learning from examples and rules. Ph.D. thesis, Paris Dauphine University
• Marc Schoenauer, Michèle Sebag (1990). Incremental Learning of Rules and Meta-rules. ML 1990
• Michèle Sebag, Marc Schoenauer (1992). Learning to Control Inconsistent Knowledge. ECAI 1992
• Michèle Sebag (1997). Stochastic Heuristics for Machine Learning & Machine Learning for Stochastic Optimization. Habilitation, Paris-Sud 11 University

2000 ...

• Céline Rouveirol, Michèle Sebag (eds.) (2001). Inductive Logic Programming - 11th International Conference, ILP 2001 Strasbourg, France, September 9–11, 2001 Proceedings. Lecture Notes in Computer Science, Vol. 2157, Springer

Jérôme Maloberti, Michèle Sebag (2001). θ-Subsumption in a Constraint Satisfaction Perspective. ILP 2001

2005 ...

• Sylvain Gelly, Nicolas Bredèche, Michèle Sebag (2005). From Factorial and Hierarchical HMM to Bayesian Network : A Representation Change Algorithm. Proceedings of the Symposium on Abstraction, Reformulation and Approximation 2005, p107-120 (SARA 2005). Lecture Notes in Computer Science, Vol. 3607
• Marc Schoenauer, Michèle Sebag (2006). Using Domain Knowledge in Evolutionary System Identification. arXiv:cs/0602021

2010 ...

• Romaric Gaudel, Michèle Sebag (2010). Feature Selection as a one-player game. ICML 2010, pdf
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2010). Dominance-Based Pareto-Surrogate for Multi-Objective Optimization. SEAL 2010, pdf
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2010). A mono surrogate for multiobjective optimization. GECCO 2010, pdf
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2011). Adaptive coordinate descent. GECCO 2011, pdf
• Sylvain Gelly, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Levente Kocsis, David Silver, Csaba Szepesvári (2012). The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions. Communications of the ACM, Vol. 55, No. 3, pdf preprint
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2012). Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. arXiv:1204.2356
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2012). Alternative Restart Strategies for CMA-ES. arXiv:1207.0206
• Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag (2012). Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. arXiv:1210.0374
• Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2013). KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. arXiv:1308.2655

2015 ...

• Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent (2017). Toward Optimal Run Racing: Application to Deep Learning Calibration. arXiv:1706.03199
• Alice Schoenauer-Sebag, Marc Schoenauer, Michèle Sebag (2017). Stochastic Gradient Descent: Going As Fast As Possible But Not Faster. arXiv:1709.01427
• Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag (2019). Automated Machine Learning with Monte-Carlo Tree Search. arXiv:1906.00170

External Links

• Home page of Michele Sebag
• Michèle Sebag - University of Paris-Sud 11 - videolectures.net
• Michèle Sebag - Algorithm Selection and Configuration with Monte-Carlo Tree Search, WiDS Zürich 2019: Keynote, slides as pdf, YouTube Video

References

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