Scalable Nonlinear Learning with Adaptive Polynomial Expansions

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

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Scalable Nonlinear Learning with Adaptive Polynomial Expansions

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arxiv 1410.0440 v1 pith:HAY55XF7 submitted 2014-10-02 cs.LG stat.ML

Alekh Agarwal , Alina Beygelzimer , Daniel Hsu , John Langford , Matus Telgarsky This is my paper

classification cs.LGstat.ML

keywords algorithmlearninglinearnonlinearabilityadaptiveadaptivelybase

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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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