Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Abstract
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, this topic remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized algorithm selection (ML operations) in a fixed ML pipeline structure. BLDS performs multi-fidelity optimization for selecting ML algorithms trained with smaller computational overhead, while controlling its pipeline search based on multi-armed bandit and limited discrepancy search. Our experiments on well-known benchmarks show that BLDS is superior to competing algorithms.
Cite
Text
Kishimoto et al. "Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization." ICML 2021 Workshops: AutoML, 2021.Markdown
[Kishimoto et al. "Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization." ICML 2021 Workshops: AutoML, 2021.](https://mlanthology.org/icmlw/2021/kishimoto2021icmlw-bandit/)BibTeX
@inproceedings{kishimoto2021icmlw-bandit,
title = {{Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization}},
author = {Kishimoto, Akihiro and Bouneffouf, Djallel and Marinescu, Radu and Ram, Parikshit and Rawat, Ambrish and Wistuba, Martin and Palmes, Paulito Pedregosa and Botea, Adi},
booktitle = {ICML 2021 Workshops: AutoML},
year = {2021},
url = {https://mlanthology.org/icmlw/2021/kishimoto2021icmlw-bandit/}
}