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1
Robustly learning mixtures of k arbitrary Gaussians:
, In:
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
,
Bakshi, Ainesh
;
Diakonikolas, Ilias
;
Jia, He
... - p. 1234-1247 , 2022
Link:
https://dl.acm.org/doi/10.1145/3519935.3519953
RT T1 <cTitle>Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing</cTitle>: T1 <Title>Robustly learning mixtures of k arbitrary Gaussians</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3519953&Exemplar=1&LAN=DE A1 Bakshi, Ainesh A1 Diakonikolas, Ilias A1 Jia, He A1 Kane, Daniel M. A1 Kothari, Pravesh K. A1 Vempala, Santosh S. PB ACM YR 2022 K1 Gaussian Mixture Model K1 Robust Statistics K1 Sum-of-Squares K1 Tensor Decomposition K1 Theory of computation K1 Mathematical optimization K1 Machine learning theory K1 Unsupervised learning and clustering K1 Semidefinite programming SP 1234 OP 1247 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3519935.3519953 DO https://dl.acm.org/doi/10.1145/3519935.3519953 SF ELIB - SuUB Bremen
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