AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
摘要
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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引用它的顶会 Paper3
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它引用的顶会 Paper5
- Bandits with many optimal armsRianne de Heide, James Cheshire, Pierre Ménard, Alexandra CarpentierNeurIPS 2021 · 被引用 28 次
- On Performance Estimation in Automatic Algorithm ConfigurationShengcai Liu, Ke Tang, Yunwen Lei, Xin YaoAAAI 2020 · 被引用 25 次
- Refined bounds for algorithm configuration: The knife-edge of dual class approximabilityMaria-Florina Balcan, Tuomas Sandholm, Ellen VitercikICML 2020 · 被引用 16 次
- Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite BudgetJasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke HüllermeierNeurIPS 2022 · 被引用 9 次
- ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite PoolGellért Weisz, András György, Wei-I Lin, Devon R. Graham 等NeurIPS 2020 · 被引用 7 次
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