# Robust Polynomial Regression up to the Information Theoretic Limit

@article{Kane2017RobustPR,
title={Robust Polynomial Regression up to the Information Theoretic Limit},
author={Daniel M. Kane and Sushrut Karmalkar and Eric Price},
journal={2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)},
year={2017},
pages={391-402}
}
• Published 10 August 2017
• Mathematics, Computer Science
• 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)
We consider the problem of robust polynomial regression, where one receives samples that are usually within a small additive error of a target polynomial, but have a chance of being arbitrary adversarial outliers. Previously, it was known how to efficiently estimate the target polynomial only when the outlier probability was subconstant in the degree of the target polynomial. We give an algorithm that works for the entire feasible range of outlier probabilities, while simultaneously improving…
9 Citations

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