921 citations · 997 across the 5 of their papers we have counts for
9 papers
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Yaodong Yu, Zitong Yang, Edgar Dobriban +2
Adversarially trained models exhibit a large generalization gap: they can interpolate the training set even for large perturbation radii, but at the cost of large test error on cle…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
Boundary thickness and robustness in learning models
Yaoqing Yang, Rajiv Khanna, Yaodong Yu +5
Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thi…
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu, Kwan Ho Ryan Chan, Chong You +2
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{M…
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Zitong Yang, Yaodong Yu, Chong You +2
The classical bias-variance trade-off predicts that bias decreases and variance increase with model complexity, leading to a U-shaped risk curve. Recent work calls this into questi…
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao +3
We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although this problem has been wi…