Robust Estimators in High Dimensions without the Computational Intractability

Robust Estimators in High Dimensions without the Computational Intractability

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We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an epsilon fraction of the samples. Such questions have a rich history spanning statistics, machine learning and theoretical computer science. Even in the most basic settings,...

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Detalhes bibliográficos
Principais autores: Diakonikolas, Ilias, Kane, Daniel M., Stewart, Alistair, Kamath, Gautam Chetan, Li, Jerry Zheng, Moitra, Ankur
Outros Autores: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Formato: Artigo
Publicado em: Institute of Electrical and Electronics Engineers (IEEE) 2018
Acesso em linha:http://hdl.handle.net/1721.1/115939
https://orcid.org/0000-0003-0048-2559
https://orcid.org/0000-0002-9937-0049
https://orcid.org/0000-0001-7047-0495

Internet

http://hdl.handle.net/1721.1/115939
https://orcid.org/0000-0003-0048-2559
https://orcid.org/0000-0002-9937-0049
https://orcid.org/0000-0001-7047-0495

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