SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

1 code implementation • 7 Dec 2021 • Yuege Xie, Bobby Shi, Hayden Schaeffer, Rachel Ward

Inspired by the success of the iterative magnitude pruning technique in finding lottery tickets of neural networks, we propose a new method -- Sparser Random Feature Models via IMP (ShRIMP) -- to efficiently fit high-dimensional data with inherent low-dimensional structure in the form of sparse variable dependencies.

Additive models Computational Efficiency +1