4 papers
-Consistency Estimation Error of Surrogate Loss Minimizers
Pranjal Awasthi, Anqi Mao, Mehryar Mohri +1
We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as -consistency estimation error bounds, sin…
A Finer Calibration Analysis for Adversarial Robustness
Pranjal Awasthi, Anqi Mao, Mehryar Mohri +1
We present a more general analysis of -calibration for adversarially robust classification. By adopting a finer definition of calibration, we can cover settings beyond the restr…
Calibration and Consistency of Adversarial Surrogate Losses
Pranjal Awasthi, Natalie Frank, Anqi Mao +2
Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the…
Variational training of neural network approximations of solution maps for physical models
Yingzhou Li, Jianfeng Lu, Anqi Mao
A novel solve-training framework is proposed to train neural network in representing low dimensional solution maps of physical models. Solve-training framework uses the neural netw…