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ORCIDYuan Cao 0006
Person information
- affiliation: University of California, Los Angeles, Department of Computer Science, CA, USA
- affiliation (PhD): Princeton University, Department of Operations Research and Financial Engineering, NJ, USA
Other persons with the same name
- Yuan Cao — disambiguation page
- Yuan Cao 0001
![[0000-0002-3089-0046]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-3089-0046")
— Changsha University of Science and Technology, School of Mathematics and Statistics, China (and 2 more) - Yuan Cao 0002 — Beijing Jiaotong University, National Engineering Research Center of Rail Transportation, Operation and Control System, China
- Yuan Cao 0003 — Hohai University, College of Internet of Things Engineering, Changzhou, China (and 3 more)
- Yuan Cao 0004 — University of Alabama, Department of Electrical and Computer Engineering, College of Engineering, Tuscaloosa, AL, USA
- Yuan Cao 0005 — Ocean University of China, School of Information Science and Engineering, China (and 2 more)
- Yuan Cao 0007 — Google Research, Mountain View, CA, USA (and 1 more)
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2020 – today
- 2023
[c21]Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu:
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. COLT 2023: 5699-5753- [c20]Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu:
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. ICLR 2023 - [c19]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. ICLR 2023 - [c18]Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu:
Understanding Train-Validation Split in Meta-Learning with Neural Networks. ICLR 2023 - [c17]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
The Benefits of Mixup for Feature Learning. ICML 2023: 43423-43479 - [c16]Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. UAI 2023: 313-323 - [i20]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
The Benefits of Mixup for Feature Learning. CoRR abs/2303.08433 (2023) - [i19]Xuran Meng, Yuan Cao, Difan Zou:
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data. CoRR abs/2303.17940 (2023) - [i18]Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu:
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. CoRR abs/2306.11680 (2023) - [i17]Xuran Meng, Difan Zou, Yuan Cao:
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. CoRR abs/2310.01975 (2023) - 2022
- [c15]Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. NeurIPS 2022 - [i16]Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR abs/2202.06526 (2022) - 2021
- [c14]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR 2021 - [c13]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. ICML 2021: 3417-3426 - [c12]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: 3427-3438 - [c11]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. IJCAI 2021: 2205-2211 - [c10]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS 2021: 8407-8418 - [i15]Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. CoRR abs/2101.01152 (2021) - [i14]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR abs/2104.13628 (2021) - [i13]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR abs/2108.11371 (2021) - [i12]Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. CoRR abs/2112.15250 (2021) - 2020
- [j1]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109(3): 467-492 (2020) - [c9]Yuan Cao, Quanquan Gu:
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. AAAI 2020: 3349-3356 - [c8]Dongruo Zhou, Yuan Cao, Quanquan Gu:
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS 2020: 4430-4440 - [c7]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: 3267-3275 - [c6]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS 2020 - [c5]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. NeurIPS 2020 - [i11]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR abs/2002.04026 (2020) - [i10]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. CoRR abs/2005.14426 (2020) - [i9]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. CoRR abs/2010.00539 (2020)
2010 – 2019
- 2019
- [c4]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. NeurIPS 2019: 10611-10621 - [c3]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. NeurIPS 2019: 10835-10845 - [c2]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. NeurIPS 2019: 14769-14779 - [i8]Yuan Cao, Quanquan Gu:
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. CoRR abs/1902.01384 (2019) - [i7]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. CoRR abs/1905.13210 (2019) - [i6]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. CoRR abs/1910.02934 (2019) - [i5]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. CoRR abs/1911.05059 (2019) - [i4]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR abs/1911.12360 (2019) - [i3]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. CoRR abs/1912.01198 (2019) - 2018
- [c1]Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang:
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. ICML 2018: 3253-3262 - [i2]Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR abs/1808.05671 (2018) - [i1]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR abs/1811.08888 (2018)
Coauthor Index
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