mlsanityData to 7 Oct 2026

AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration

arXiv 2212.00333 ↗First posted 1 Dec 2022cs.LG cs.DScited by 6

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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

  1. 2015Non-stochastic Best Arm Identification and Hyperparameter Optimizationcited by 219
  2. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  3. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  4. 2018Preference-based Online Learning with Dueling Bandits: A Surveycited by 100
  5. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 88
  6. 2022A Survey of Methods for Automated Algorithm Configurationcited by 29
  7. 2019How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm designcited by 27
  8. 2021Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedbackcited by 19
  9. 2021Bandits with many optimal armscited by 16
  10. 2018Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidencecited by 15
  11. 2018LeapsAndBounds: A Method for Approximately Optimal Algorithm Configurationcited by 13
  12. 2020Refined bounds for algorithm configuration: The knife-edge of dual class approximabilitycited by 12
6 more
  1. 2019Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configurationcited by 9
  2. 2019On Performance Estimation in Automatic Algorithm Configurationcited by 8
  3. 2013Bayesian Optimization With Censored Response Datacited by 6
  4. 2022Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budgetcited by 5
  5. 2019On the Impact of the Cutoff Time on the Performance of Algorithm Configuratorscited by 3
  6. 2020Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operatorscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.