@inproceedings{agarwal2014scalable,
author = {Agarwal, Alekh and Beygelzimer, Alina and Langford, John and Hsu, Daniel and Telgarsky, Matus},
title = {Scalable Nonlinear Learning with Adaptive Polynomial Expansions},
booktitle = {Advances in Neural Information Processing Systems 27 (NIPS 2014)},
year = {2014},
month = {October},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/scalable-nonlinear-learning-with-adaptive-polynomial-expansions/},
}