175 citations · 317 across the 46 of their papers we have counts for
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cs.LG2021★ 2 cited
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning
Yuege Xie, Bobby Shi, Hayden Schaeffer +1
Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between vari…
stat.ML2021★ 6 cited
Conditioning of Random Feature Matrices: Double Descent and Generalization Error
Zhijun Chen, Hayden Schaeffer
We provide (high probability) bounds on the condition number of random feature matrices. In particular, we show that if the complexity ratio where is the number o…
stat.ML2021★ 35 cited
Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi, Hayden Schaeffer, Robert Shi +3
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach…