• Corpus ID: 14347197

# Loss Minimization and Parameter Estimation with Heavy Tails

@article{Hsu2016LossMA,
title={Loss Minimization and Parameter Estimation with Heavy Tails},
author={Daniel J. Hsu and Sivan Sabato},
journal={J. Mach. Learn. Res.},
year={2016},
volume={17},
pages={18:1-18:40}
}
• Published 6 July 2013
• Computer Science, Mathematics
• J. Mach. Learn. Res.
This work studies applications and generalizations of a simple estimation technique that provides exponential concentration under heavy-tailed distributions, assuming only bounded low-order moments. We show that the technique can be used for approximate minimization of smooth and strongly convex losses, and specifically for least squares linear regression. For instance, our $d$-dimensional estimator requires just $\tilde{O}(d\log(1/\delta))$ random samples to obtain a constant factor…
141 Citations

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