Scalable Nonlinear Learning with Adaptive Polynomial Expansions - Agarwal, Alekh; Beygelzimer, Alina; Langford, John; Hsu, Daniel; Telgarsky, Matus (Advances in Neural Information Processing Systems 27 (NIPS 2014), 2014)
Can we eff ectively 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 tradeo ff ability compares very favorably against strong baselines.