How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design

DOI10.1145/3406325.3451036arXiv1908.02894OpenAlexW3171756615MaRDI QIDQ6065218

Maria-Florina Balcan, Tuomas W. Sandholm, Dan DeBlasio, Ellen Vitercik, Carl Kingsford, Travis Dick

Publication date: 14 November 2023

Published in: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.02894

zbMATH Keywords

computational biologymechanism designmachine learning theoryautomated algorithm configurationautomated algorithm designdata-driven algorithm design

Mathematics Subject Classification ID

Theory of computing (68Qxx)

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