Machine learning has been widely applied in many aspects, but training a machine learning model is increasingly difficult. There are more optimization problems named "black-box" where the relationship between model parameters and outcomes is uncertain or complex to trace. Currently, optimizing black-box models that need a large number of query observations and parameters becomes difficult. To overcome the drawbacks of the existing algorithms, in this study, we propose a zeroth-order method that originally came from quantum computing called the parameter-shift rule, which has used a lesser number of parameters than previous methods.
@article{arxiv.2503.13545,
title = {Optimization on black-box function by parameter-shift rule},
author = {Vu Tuan Hai},
journal= {arXiv preprint arXiv:2503.13545},
year = {2025}
}