MichC3A8le Sebag

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Michèle Sebag [1] Michèle Sebag,

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

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

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 [7]
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 [8]
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 [9]
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

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

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