Yuan Cao

According to our database, Yuan Cao authored at least 29 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data.

Xuran Meng, Difan Zou, Yuan Cao

CoRR, 2023

Per-Example Gradient Regularization Improves Learning Signals from Noisy Data.

Xuran Meng, Yuan Cao, Difan Zou

CoRR, 2023

Benign Overfitting in Adversarially Robust Linear Classification.

Jinghui Chen, Yuan Cao, Quanquan Gu

Proceedings of the Uncertainty in Artificial Intelligence, 2023

The Benefits of Mixup for Feature Learning.

Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2023

Understanding Train-Validation Split in Meta-Learning with Neural Networks.

Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization.

Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study.

Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks.

Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Benign Overfitting in Two-layer Convolutional Neural Networks.

Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu

CoRR, 2022

Benign Overfitting in Two-layer Convolutional Neural Networks.

Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures.

Yuan Cao, Quanquan Gu, Mikhail Belkin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Understanding the Spectral Bias of Deep Learning.

Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise.

Spencer Frei, Yuan Cao, Quanquan Gu

Proceedings of the 38th International Conference on Machine Learning, 2021

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins.

Spencer Frei, Yuan Cao, Quanquan Gu

Proceedings of the 38th International Conference on Machine Learning, 2021

How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?

Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Gradient descent optimizes over-parameterized deep ReLU networks.

Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

Mach. Learn., 2020

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds.

Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang

CoRR, 2020

Agnostic Learning of a Single Neuron with Gradient Descent.

Spencer Frei, Yuan Cao, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks.

Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks.

Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization.

Dongruo Zhou, Yuan Cao, Quanquan Gu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks.

Yuan Cao, Quanquan Gu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks.

Yuan Cao, Quanquan Gu

CoRR, 2019

Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks.

Spencer Frei, Yuan Cao, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks.

Yuan Cao, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks.

Yuan Cao, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.

Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

CoRR, 2018

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization.

Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu

CoRR, 2018

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference.

Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang

Proceedings of the 35th International Conference on Machine Learning, 2018