212 citations · 799 across the 27 of their papers we have counts for
4 papers · 1 filter
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui Chen, Yuan Cao, Quanquan Gu
"Benign overfitting", where classifiers memorize noisy training data yet still achieve a good generalization performance, has drawn great attention in the machine learning communit…
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Difan Zou, Yuan Cao, Yuanzhi Li +1
Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed that compared with (stochastic) gradient desce…
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
Yuan Cao, Quanquan Gu, Mikhail Belkin
Modern machine learning systems such as deep neural networks are often highly over-parameterized so that they can fit the noisy training data exactly, yet they can still achieve sm…
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei, Yuan Cao, Quanquan Gu
We consider a one-hidden-layer leaky ReLU network of arbitrary width trained by stochastic gradient descent (SGD) following an arbitrary initialization. We prove that SGD produces…