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ROBUST ESTIMATORS IN HIGH-DIMENSIONS WITHOUT THE COMPUTATIONAL INTRACTABILITY
- Source :
- SIAM JOURNAL ON COMPUTING, vol 48, iss 2, SIAM Journal on Computing, vol 48, iss 2
- Publication Year :
- 2019
- Publisher :
- eScholarship, University of California, 2019.
-
Abstract
- We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the samples. Such questions have a rich hist...
- Subjects :
- Peace
General Computer Science
Distribution (number theory)
Computer science
product distributions
General Mathematics
Gaussian distribution
Estimator
Computation Theory and Mathematics
0102 computer and information sciences
robust learning
Adversary
Mixture model
01 natural sciences
Pure Mathematics
Justice and Strong Institutions
Computation Theory & Mathematics
Robust learning
010201 computation theory & mathematics
mixture models
high-dimensions
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- SIAM JOURNAL ON COMPUTING, vol 48, iss 2, SIAM Journal on Computing, vol 48, iss 2
- Accession number :
- edsair.doi.dedup.....4445de9b946ff0ce37bbe3c1fbae3310