adaptive polynomial expansion
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J. Telgarsky
Neural Information Processing Systems http://nips.cc/
Scalable Non-linear Learning with Adaptive Polynomial Expansions
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.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J. Telgarsky
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.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
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.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Agarwal, Alekh, Beygelzimer, Alina, Hsu, Daniel J., Langford, John, Telgarsky, Matus J.
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. Papers published at the Neural Information Processing Systems Conference.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Agarwal, Alekh, Beygelzimer, Alina, Hsu, Daniel J., Langford, John, Telgarsky, Matus J.
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.