Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter Optimization
This work addresses hyperparameter optimization for machine learning practitioners, but it is incremental as it builds on an existing method.
The paper tackles hyperparameter optimization by proposing modifications to the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm, and experimental results show that these modifications significantly improve ATPE's effectiveness on standard benchmarks.
In this paper, we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm. We propose several modifications to ATPE and assess their efficacy on a diverse set of standard benchmark functions. Experimental results demonstrate that the proposed modifications significantly improve the effectiveness of ATPE hyperparameter optimization on selected benchmarks, a finding that holds practical relevance for their application in real-world machine learning / optimization tasks.