Yuan Cao

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 40

Top Topics

Stochastic Gradient Descent

Generalization Bounds

Top Venues

Publications

Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. Trans. Mach. Learn. Res. 2024 (2024)
Chenyang Zhang, Difan Zou, Yuan Cao
The Implicit Bias of Adam on Separable Data. CoRR (2024)
Jinghui Chen, Yuan Cao, Quanquan Gu
Benign Overfitting in Adversarially Robust Linear Classification. UAI (2023)
Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. ICLR (2023)
Xuran Meng, Difan Zou, Yuan Cao
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. CoRR (2023)
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. ICLR (2023)
Xuran Meng, Yuan Cao, Difan Zou
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data. CoRR (2023)
Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu
Understanding Train-Validation Split in Meta-Learning with Neural Networks. ICLR (2023)
Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu
Benign Overfitting in Two-layer Convolutional Neural Networks. NeurIPS (2022)
Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR (2022)
Spencer Frei, Yuan Cao, Quanquan Gu
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. ICML (2021)
Jinghui Chen, Yuan Cao, Quanquan Gu
Benign Overfitting in Adversarially Robust Linear Classification. CoRR (2021)
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR (2021)
Yuan Cao, Quanquan Gu, Mikhail Belkin
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR (2021)
Spencer Frei, Yuan Cao, Quanquan Gu
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. ICML (2021)
Yuan Cao, Quanquan Gu, Mikhail Belkin
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS (2021)
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR (2021)
Spencer Frei, Yuan Cao, Quanquan Gu
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. CoRR (2021)
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
Towards Understanding the Spectral Bias of Deep Learning. IJCAI (2021)
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI (2020)
Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR (2020)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109 (3) (2020)
Spencer Frei, Yuan Cao, Quanquan Gu
Agnostic Learning of a Single Neuron with Gradient Descent. NeurIPS (2020)
Dongruo Zhou, Yuan Cao, Quanquan Gu
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS (2020)
Spencer Frei, Yuan Cao, Quanquan Gu
Agnostic Learning of a Single Neuron with Gradient Descent. CoRR (2020)
Spencer Frei, Yuan Cao, Quanquan Gu
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. CoRR (2020)
Yuan Cao, Quanquan Gu
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. AAAI (2020)
Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS (2020)
Spencer Frei, Yuan Cao, Quanquan Gu
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. NeurIPS (2019)
Yuan Cao, Quanquan Gu
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. NeurIPS (2019)
Yuan Cao, Quanquan Gu
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. CoRR (2019)
Spencer Frei, Yuan Cao, Quanquan Gu
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. CoRR (2019)
Yuan Cao, Quanquan Gu
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. CoRR (2019)
Yuan Cao, Quanquan Gu
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. NeurIPS (2019)
Yuan Cao, Quanquan Gu
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. CoRR (2019)
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR (2019)
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
Towards Understanding the Spectral Bias of Deep Learning. CoRR (2019)
Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. ICML (2018)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR (2018)
Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR (2018)