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Loss minimization and parameter estimation with heavy tails
Daniel Hsu,
Sivan Sabato
Department of Computer Science
Research output
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Contribution to journal
›
Article
›
peer-review
117
Scopus citations
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Dive into the research topics of 'Loss minimization and parameter estimation with heavy tails'. Together they form a unique fingerprint.
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Keyphrases
Parameter Estimation
100%
Metric Space
100%
Constant-factor Approximation Algorithm
100%
Estimation Method
100%
Work Study
100%
Low-rank
100%
Further Application
100%
Noise Distribution
100%
Heavy Tails
100%
Strongly Convex
100%
Loss Factor
100%
Heavy-tailed Distribution
100%
Numerical Minimization
100%
Covariance Matrix Estimation
100%
Lower-order Moments
100%
Covariate Distribution
100%
Non-convex Cost Function
100%
Sparse Regression
100%
Simple Estimation Method
100%
Median Estimation
100%
Least Squares Linear Regression
100%
Exponential Concentration
100%
Least Squares Loss
100%
Loss Minimization
100%
Sub-Gaussian
100%
Mathematics
Parameter Estimation
100%
Least Square
100%
Covariate
100%
Linear Regression
100%
Heavy Tail
100%
Approximates
50%
Probability Theory
50%
Metric Space
50%
Constant Factor
50%
Random Sample
50%
Covariance Matrix Estimation
50%
Heavy-Tailed Distribution
50%
Square Loss
50%