Scalable Non-linear Learning with Adaptive Polynomial Expansions

Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014 Neural Information Processing Systems  
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.
dblp:conf/nips/AgarwalBHLT14 fatcat:phv5g2nx6ffttm7k646zt4fctu