Machine Learning for Online Algorithm Selection under Censored Feedback

A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).

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Abstract
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.
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Proceedings of the 36th AAAI Conference on Artificial Intelligence
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Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022.
Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI.
@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }
Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI, 2022.

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