  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.
