AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Keywords:SO: Algorithm Configuration, ML: Online Learning & Bandits
AbstractWe 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.
How to Cite
Brandt, J., Schede, E., Haddenhorst, B., Bengs, V., Hüllermeier, E., & Tierney, K. (2023). AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 12355-12363. https://doi.org/10.1609/aaai.v37i10.26456
AAAI Technical Track on Search and Optimization