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stat.ML2023
Generalization Bound for Diffusion Models using Random Features
Esha Saha, Giang Tran
Diffusion probabilistic models have been successfully used to generate data from noise. However, most diffusion models are computationally expensive and difficult to interpret with…
stat.ML2022★ 13 cited
HARFE: Hard-Ridge Random Feature Expansion
Esha Saha, Hayden Schaeffer, Giang Tran
We propose a random feature model for approximating high-dimensional sparse additive functions called the hard-ridge random feature expansion method (HARFE). This method utilizes a…
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…