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A simple dynamic bandit algorithm for hyper-parameter tuning
Abstract : Hyper-parameter tuning is a major part of modern machine learning systems. The tuning itself can be seen as a sequential resource allocation problem. As such, methods for multi-armed bandits have been already applied. In this paper, we view hyper-parameter optimization as an instance of best-arm identification in infinitely many-armed bandits. We propose D-TTTS, a new adaptive algorithm inspired by Thompson sampling, which dynamically balances between refining the estimate of the quality of hyper-parameter configurations previously explored and adding new hyper-parameter configurations to the pool of candidates. The algorithm is easy to implement and shows competitive performance compared to state-of-the-art algorithms for hyper-parameter tuning.
Cited literature [25 references]
https://hal.inria.fr/hal-02145200
Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Saturday, June 1, 2019 - 11:49:38 PM
Last modification on : Tuesday, January 4, 2022 - 6:14:25 AM
Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Saturday, June 1, 2019 - 11:49:38 PM
Last modification on : Tuesday, January 4, 2022 - 6:14:25 AM
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shang2019simple.pdf
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