AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
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.
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Cited by top-tier papers3
- Utilitarian Algorithm ConfigurationDevon R. Graham, Kevin Leyton-Brown, Tim RoughgardenNeurIPS 2023 · 2 citations
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- Bandits with many optimal armsRianne de Heide, James Cheshire, Pierre Ménard, Alexandra CarpentierNeurIPS 2021 · 28 citations
- On Performance Estimation in Automatic Algorithm ConfigurationShengcai Liu, Ke Tang, Yunwen Lei, Xin YaoAAAI 2020 · 25 citations
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- ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite PoolGellért Weisz, András György, Wei-I Lin, Devon R. Graham et al.NeurIPS 2020 · 7 citations
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