Adaptive multi-fidelity optimization with fast learning rates

Fiegel, Côme; Gabillon, Victor; Valko, Michal

Document type :

Communication dans un congrès avec actes

Title :

Adaptive multi-fidelity optimization with fast learning rates

Author(s) :

Fiegel, Côme [Auteur]
Scool [Scool]
Département d'informatique - ENS-PSL [DI-ENS]
Gabillon, Victor [Auteur]
Huawei R&D [United Kingdom]
Valko, Michal [Auteur]

Conference title :

International Conference on Artificial Intelligence and Statistics

City :

Palermo

Country :

Italie

Start date of the conference :

2020

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

In multi-fidelity optimization, we have access to biased approximations of varying costs of the target function. In this work, we study the setting of optimizing a locally smooth function with a limited budget Λ, where the ...
Show more >In multi-fidelity optimization, we have access to biased approximations of varying costs of the target function. In this work, we study the setting of optimizing a locally smooth function with a limited budget Λ, where the learner has to make a trade-off between the cost and the bias of these approximations. We first prove lower bounds for the simple regret under different assumptions on the fidelities, based on a cost-to-bias function. We then present the Kometo algorithm which achieves, with additional logarithmic factors, the same rates without any knowledge of the function smoothness and fidelity assumptions and improving prior results. Finally, we empirically show that our algorithm outperforms prior multi-fidelity optimization methods without the knowledge of problem-dependent parameters.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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