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c-TPE: Generalizing Tree-structured Parzen Estimator with Inequality Constraints for Continuous and Categorical Hyperparameter Optimization

2022-11-26 00:25:11
Shuhei Watanabe, Frank Hutter
arXiv_AI
     
Abstract

Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms. A widely-used versatile HPO method is a variant of Bayesian optimization called tree-structured Parzen estimator (TPE), which splits data into good and bad groups and uses the density ratio of those groups as an acquisition function (AF). However, real-world applications often have some constraints, such as memory requirements, or latency. In this paper, we present an extension of TPE to constrained optimization (c-TPE) via simple factorization of AFs. The experiments demonstrate c-TPE is robust to various constraint levels and exhibits the best average rank performance among existing methods with statistical significance on search spaces with categorical parameters on 81 settings.

Abstract (translated)

URL

https://arxiv.org/abs/2211.14411

PDF

https://arxiv.org/pdf/2211.14411.pdf

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