AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
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Abstract (arXiv)
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. 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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 1 Dec 2022.
Builds on
18 of 43 references.
References · 18
- 2015Non-stochastic Best Arm Identification and Hyperparameter Optimizationcited by 219
- 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
- 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
- 2018Preference-based Online Learning with Dueling Bandits: A Surveycited by 100
- 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 88
- 2022A Survey of Methods for Automated Algorithm Configurationcited by 29
- 2019How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm designcited by 27
- 2021Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedbackcited by 19
- 2021Bandits with many optimal armscited by 16
- 2018Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidencecited by 15
- 2018LeapsAndBounds: A Method for Approximately Optimal Algorithm Configurationcited by 13
- 2020Refined bounds for algorithm configuration: The knife-edge of dual class approximabilitycited by 12
6 more
- 2019Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configurationcited by 9
- 2019On Performance Estimation in Automatic Algorithm Configurationcited by 8
- 2013Bayesian Optimization With Censored Response Datacited by 6
- 2022Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budgetcited by 5
- 2019On the Impact of the Cutoff Time on the Performance of Algorithm Configuratorscited by 3
- 2020Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operatorscited by 2
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.
6 papers.
- 2024Utilitarian Algorithm Configuration for Infinite Parameter Spacescited by 2
- 2023Utilitarian Algorithm Configurationcited by 1
- 2023Identifying Copeland Winners in Dueling Bandits with Indifferencescited by 0
- 2025Practical, Utilitarian Algorithm Configurationcited by 0
- 2026Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimizationcited by 0
- 2026DGA2D: Directed Graph-Guided Automated Algorithm Design with Large Language Modelscited by 0
- 2026DGA2D: Directed Graph-Guided Automated Algorithm Design with Large Language Models1 Aug 2026
- 2026Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization3 Jun 2026
- 2025Practical, Utilitarian Algorithm Configuration16 Oct 2025
- 2024Utilitarian Algorithm Configuration for Infinite Parameter Spaces28 May 2024
- 2023Utilitarian Algorithm Configuration31 Oct 2023
- 2023Identifying Copeland Winners in Dueling Bandits with Indifferences1 Oct 2023