# Scalable Nonlinear Learning with Adaptive Polynomial Expansions

arXiv: https://arxiv.org/abs/1410.0440 · HTML: https://mlsanity.com/paper/1410.0440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2014.

## Built on by (most cited first, 1 of 1)

- [ChaCha for Online AutoML](https://mlsanity.com/paper/2106.04815/) (2021)

## Abstract (arXiv)

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.