Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing
| Autor: | Bourki, Amine, Coulm, Matthieu, Rolet, Philippe, Teytaud, Olivier, Vayssière, Paul |
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| Přispěvatelé: | Laboratoire de Recherche et de Développement de l'EPITA (LRDE), Ecole Pour l'Informatique et les Techniques Avancées (EPITA), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Machine Learning and Optimisation (TAO), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, ANR-08-COSI-0007,OMD2,Optimisation multi-disciplinaire distribuée(2008), Teytaud, Olivier, Optimisation multi-disciplinaire distribuée - - OMD22008 - ANR-08-COSI-0007 - COSINUS - VALID, Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: | |
| Zdroj: | ICINCO 2010 ICINCO2010 ICINCO2010, 2010, funchal madeira, Portugal. pp.10 |
| Popis: | International audience; ``Simple regret'' algorithms are designed for noisy optimization in unstructured domains. In particular, this literature has shown that the uniform algorithm is indeed optimal asymptotically and suboptimal non-asymptotically. We investigate theoretically and experimentally the application of these algorithms, for automatic parameter tuning, in particular from the point of view of the number of samples required for ``uniform'' to be relevant and from the point of view of statistical guarantees. We see that for moderate numbers of arms, the possible improvement in terms of computational power required for statistical validation can't be more than linear as a function of the number of arms and provide a simple rule to check if the simple uniform algorithm (trivially parallel) is relevant. Our experiments are performed on the tuning of a Monte-Carlo Tree Search algorithm, a great recent tool for high-dimensional planning with particularly impressive results for difficult games and in particular the game of Go. |
| Databáze: | OpenAIRE |
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