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COLT
2008
Springer

Polynomial Regression under Arbitrary Product Distributions

14 years 10 months ago
Polynomial Regression under Arbitrary Product Distributions
In recent work, Kalai, Klivans, Mansour, and Servedio [KKMS05] studied a variant of the "Low-Degree (Fourier) Algorithm" for learning under the uniform probability distribution on {0, 1}n . They showed that the L1 polynomial regression algorithm yields agnostic (tolerant to arbitrary noise) learning algorithms with respect to the class of threshold functions -- under certain restricted instance distributions, including uniform on {0, 1}n and Gaussian on Rn . In this work we show how all learning results based on the Low-Degree Algorithm can be generalized to give almost identical agnostic guarantees under arbitrary product distributions on instance spaces X1 ׷
Eric Blais, Ryan O'Donnell, Karl Wimmer
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2008
Where COLT
Authors Eric Blais, Ryan O'Donnell, Karl Wimmer
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