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Meta online learning: experiments on a unit commitment problem

Jialin Liu 1, 2 Olivier Teytaud 1, 2 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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.
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https://hal.inria.fr/hal-00973397
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Submitted on : Friday, April 4, 2014 - 10:50:58 AM
Last modification on : Saturday, June 25, 2022 - 10:12:59 PM
Long-term archiving on: : Friday, July 4, 2014 - 11:30:55 AM

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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. ⟨hal-00973397⟩

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