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Autonomous learning of parameters in differential equations

Abstract : We propose EDEN+ 1 a fully automatic learner of parameters in dynamical systems that selects automatically the next experiment to do in the laboratory to improve its performance. EDEN+ improves upon EDEN, an experimental design algorithm proposed in the context of DREAM 6 and 7 challenges, with several new features: ability to take into account experiments with different costs, Monte-Carlo Tree Search parallelization, global optimization for parameter estimation. An illustration of the behaviour of EDEN+ is given on one of the DREAM7 challenging problems.
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Submitted on : Wednesday, February 10, 2016 - 4:54:31 PM
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  • HAL Id : hal-01272370, version 1


Adel Mezine, Artémis Llamosi, Véronique Letort, Michèle Sebag, Florence d'Alché-Buc. Autonomous learning of parameters in differential equations. 32nd International Conference on Machine Learning (ICML) - AutoML workshop, Jul 2015, Lille, France. ⟨hal-01272370⟩



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