62 citations · 146 across the 11 of their papers we have counts for
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cs.LG2020★ 28 cited
Multiple Descent: Design Your Own Generalization Curve
Lin Chen, Yifei Min, Mikhail Belkin +1
This paper explores the generalization loss of linear regression in variably parameterized families of models, both under-parameterized and over-parameterized. We show that the gen…
cs.LG2020★ 30 cited
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Yifei Min, Lin Chen, Amin Karbasi
Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy.…
cs.LG2020★ 19 cited
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen, Yifei Min, Mingrui Zhang +1
Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the d…