AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Abstract
We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Although this field of research has experienced much progress recently regarding approaches satisfying strong theoretical guarantees, there is still a gap between the practical performance of these approaches and the heuristic state-of-the-art approaches. Recently, there has been significant progress in designing AC approaches that satisfy strong theoretical guarantees. However, a significant gap still remains between the practical performance of these approaches and state-of-the-art heuristic methods. To this end, we introduce AC-Band, a general approach for the AC problem based on multi-armed bandits that provides theoretical guarantees while exhibiting strong practical performance. We show that AC-Band requires significantly less computation time than other AC approaches providing theoretical guarantees while still yielding high-quality configurations.
Cite
Text
Brandt et al. "AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I10.26456Markdown
[Brandt et al. "AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/brandt2023aaai-ac/) doi:10.1609/AAAI.V37I10.26456BibTeX
@inproceedings{brandt2023aaai-ac,
title = {{AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration}},
author = {Brandt, Jasmin and Schede, Elias and Haddenhorst, Björn and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {12355-12363},
doi = {10.1609/AAAI.V37I10.26456},
url = {https://mlanthology.org/aaai/2023/brandt2023aaai-ac/}
}