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

MCML Authors

Viktor Bengs

Dr.

* Former Member

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Core PI

Artificial Intelligence and Machine Learning

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.

inproceedings BSH+23

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AAAI 2023

37th Conference on Artificial Intelligence. Washington, DC, USA, Feb 07-14, 2023.

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Authors

J. Brandt • E. Schede • B. Haddenhorst • V. Bengs • E. Hüllermeier • K. Tierney

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Links

DOI

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Research Area

 A3 | Computational Models