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How much data is sufficient to learn high-performing algori..:
, In:
Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
,
Balcan, Maria-Florina
;
DeBlasio, Dan
;
Dick, Travis
... - p. 919-932 , 2021
Link:
https://dl.acm.org/doi/10.1145/3406325.3451036
RT T1 <cTitle>Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing</cTitle>: T1 <Title>How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3451036&Exemplar=1&LAN=DE A1 Balcan, Maria-Florina A1 DeBlasio, Dan A1 Dick, Travis A1 Kingsford, Carl A1 Sandholm, Tuomas A1 Vitercik, Ellen PB ACM YR 2021 K1 Automated algorithm design K1 automated algorithm configuration K1 computational biology K1 data-driven algorithm design K1 machine learning theory K1 mechanism design K1 Theory of computation K1 Theory and algorithms for application domains K1 Machine learning theory K1 Sample complexity and generalization bounds SP 919 OP 932 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3406325.3451036 DO https://dl.acm.org/doi/10.1145/3406325.3451036 SF ELIB - SuUB Bremen
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