Scalable Non-linear Learning with Adaptive Polynomial Expansions[PDF] [BibTeX] [Supplemental] [Reviews]
Conference Event Type: Poster
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.