Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913177100353536 |
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| author | Watanabe, Shuhei |
| author_facet | Watanabe, Shuhei |
| contents | Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely used Bayesian optimization method in recent parameter tuning frameworks such as Hyperopt and Optuna. Despite its popularity, the roles of each control parameter in TPE and the algorithm intuition have not been discussed so far. The goal of this paper is to identify the roles of each control parameter and their impacts on parameter tuning based on the ablation studies using diverse benchmark datasets. The recommended setting concluded from the ablation studies is demonstrated to improve the performance of TPE. Our TPE implementation used in this paper is available at https://github.com/nabenabe0928/tpe/tree/single-opt. OptunaHub now provides our standalone TPE implementation at https://hub.optuna.org/samplers/tpe_tutorial/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_11127 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance Watanabe, Shuhei Machine Learning Artificial Intelligence Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely used Bayesian optimization method in recent parameter tuning frameworks such as Hyperopt and Optuna. Despite its popularity, the roles of each control parameter in TPE and the algorithm intuition have not been discussed so far. The goal of this paper is to identify the roles of each control parameter and their impacts on parameter tuning based on the ablation studies using diverse benchmark datasets. The recommended setting concluded from the ablation studies is demonstrated to improve the performance of TPE. Our TPE implementation used in this paper is available at https://github.com/nabenabe0928/tpe/tree/single-opt. OptunaHub now provides our standalone TPE implementation at https://hub.optuna.org/samplers/tpe_tutorial/. |
| title | Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2304.11127 |