Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Alekh Agarwal; Alina Beygelzimer; Daniel Hsu; John Langford; Matus Telgarsky

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arXiv · 1410.0440

Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Alekh Agarwal·Alina Beygelzimer·Daniel Hsu·John Langford·Matus Telgarsky

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Abstract

Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental study shows that its computation/prediction tradeoff ability compares very favorably against strong baselines.

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Alekh Agarwal, Alina Beygelzimer, Daniel Hsu, John Langford, Matus Telgarsky. 2014-10-02. Scalable Nonlinear Learning with Adaptive Polynomial Expansions. https://arxiv.org/abs/1410.0440

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