Meta online learning: experiments on a unit commitment problem

Jialin Liu 1, 2 Olivier Teytaud 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Online learning is machine learning, in real time from successive data samples. Meta online learning consists in combining several online learning algorithms from a given set (termed portfolio) of algorithms. The goal can be (i) mitigating the effect of a bad choice of online learning algorithms (ii) parallelization (iii) combining the strengths of different algorithms. Basically, meta online learning boils down to combining noisy optimization algorithms. Whereas many tools exist for combining combinatorial optimization tools, little is known about combining noisy optimization algorithms. Recently, a methodology termed lag has been proposed for that. We test experimentally the lag methodology for online learning, for a stock management problem and a cartpole problem.
Type de document :
Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2014, Bruges, Belgium. 2014
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https://hal.inria.fr/hal-00973397
Contributeur : Jialin Liu <>
Soumis le : vendredi 4 avril 2014 - 10:50:58
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
Document(s) archivé(s) le : vendredi 4 juillet 2014 - 11:30:55

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portfolio2xp.pdf
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  • HAL Id : hal-00973397, version 1

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Jialin Liu, Olivier Teytaud. Meta online learning: experiments on a unit commitment problem. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2014, Bruges, Belgium. 2014. 〈hal-00973397〉

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