Yuan Cao - RankMe

63 papers A* 26A 4Journal 33

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Transformers Trained via Gradient Descent Can Provably Learn a Class of Teacher Models. CoRR Chenyang Zhang, Qingyue Zhao, Quanquan Gu, Yuan Cao
2025	A*	`conf`	On the Feature Learning in Diffusion Models. ICLR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2025	A	`conf`	On the Power of Multitask Representation Learning with Gradient Descent. AISTATS Qiaobo Li, Zixiang Chen, Yihe Deng, Yiwen Kou, Yuan Cao, Quanquan Gu
2025	J	`jnl`	On the Robustness of Transformers against Context Hijacking for Linear Classification. CoRR Tianle Li, Chenyang Zhang, Xingwu Chen, Yuan Cao, Difan Zou
2025	J	`jnl`	Per-example gradient regularization improves learning signals from noisy data. Mach. Learn. Xuran Meng, Yuan Cao, Difan Zou
2025	A	`conf`	Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. AISTATS Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki
2025	A*	`conf`	Transformer Learns Optimal Variable Selection in Group-Sparse Classification. ICLR Chenyang Zhang, Xuran Meng, Yuan Cao
2024	A*	`conf`	Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. ICML Xuran Meng, Difan Zou, Yuan Cao
2024	A*	`conf`	Global Convergence in Training Large-Scale Transformers. NeurIPS Cheng Gao, Yuan Cao, Zihao Li, Yihan He, Mengdi Wang, Han Liu, Jason M. Klusowski, Jianqing Fan
2024	J	`jnl`	Global Convergence in Training Large-Scale Transformers. CoRR Cheng Gao, Yuan Cao, Zihao Li, Yihan He, Mengdi Wang, Han Liu, Jason M. Klusowski, Jianqing Fan
2024	J	`jnl`	Initialization Matters: On the Benign Overfitting of Two-Layer ReLU CNN with Fully Trainable Layers. CoRR Shuning Shang, Xuran Meng, Yuan Cao, Difan Zou
2024	A*	`conf`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. NeurIPS Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. CoRR Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. Trans. Mach. Learn. Res. Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu
2024	J	`jnl`	On the Feature Learning in Diffusion Models. CoRR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2024	A*	`conf`	One-Layer Transformer Provably Learns One-Nearest Neighbor In Context. NeurIPS Zihao Li, Yuan Cao, Cheng Gao, Yihan He, Han Liu, Jason M. Klusowski, Jianqing Fan, Mengdi Wang
2024	J	`jnl`	One-Layer Transformer Provably Learns One-Nearest Neighbor In Context. CoRR Zihao Li, Yuan Cao, Cheng Gao, Yihan He, Han Liu, Jason M. Klusowski, Jianqing Fan, Mengdi Wang
2024	A*	`conf`	The Implicit Bias of Adam on Separable Data. NeurIPS Chenyang Zhang, Difan Zou, Yuan Cao
2024	J	`jnl`	The Implicit Bias of Adam on Separable Data. CoRR Chenyang Zhang, Difan Zou, Yuan Cao
2024	J	`jnl`	Towards Simple and Provable Parameter-Free Adaptive Gradient Methods. CoRR Yuanzhe Tao, Huizhuo Yuan, Xun Zhou, Yuan Cao, Quanquan Gu
2023	A	`conf`	Benign Overfitting in Adversarially Robust Linear Classification. UAI Jinghui Chen, Yuan Cao, Quanquan Gu
2023	J	`jnl`	Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. CoRR Xuran Meng, Difan Zou, Yuan Cao
2023	J	`jnl`	Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. CoRR Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki
2023	A*	`conf`	How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. ICLR Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu
2023	J	`jnl`	Per-Example Gradient Regularization Improves Learning Signals from Noisy Data. CoRR Xuran Meng, Yuan Cao, Difan Zou
2023	A*	`conf`	The Benefits of Mixup for Feature Learning. ICML Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2023	J	`jnl`	The Benefits of Mixup for Feature Learning. CoRR Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2023	A*	`conf`	The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. COLT Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu
2023	J	`jnl`	The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. CoRR Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu
2023	A*	`conf`	Understanding Train-Validation Split in Meta-Learning with Neural Networks. ICLR Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu
2023	A*	`conf`	Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. ICLR Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2022	A*	`conf`	Benign Overfitting in Two-layer Convolutional Neural Networks. NeurIPS Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu
2022	J	`jnl`	Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu
2021	A*	`conf`	Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. ICML Spencer Frei, Yuan Cao, Quanquan Gu
2021	J	`jnl`	Benign Overfitting in Adversarially Robust Linear Classification. CoRR Jinghui Chen, Yuan Cao, Quanquan Gu
2021	A*	`conf`	How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
2021	A*	`conf`	Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. ICML Spencer Frei, Yuan Cao, Quanquan Gu
2021	J	`jnl`	Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. CoRR Spencer Frei, Yuan Cao, Quanquan Gu
2021	A*	`conf`	Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS Yuan Cao, Quanquan Gu, Mikhail Belkin
2021	J	`jnl`	Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR Yuan Cao, Quanquan Gu, Mikhail Belkin
2021	A*	`conf`	Towards Understanding the Spectral Bias of Deep Learning. IJCAI Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2021	J	`jnl`	Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2020	A*	`conf`	A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
2020	A	`conf`	Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS Dongruo Zhou, Yuan Cao, Quanquan Gu
2020	J	`jnl`	Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. CoRR Spencer Frei, Yuan Cao, Quanquan Gu
2020	A*	`conf`	Agnostic Learning of a Single Neuron with Gradient Descent. NeurIPS Spencer Frei, Yuan Cao, Quanquan Gu
2020	J	`jnl`	Agnostic Learning of a Single Neuron with Gradient Descent. CoRR Spencer Frei, Yuan Cao, Quanquan Gu
2020	A*	`conf`	Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2020	A*	`conf`	Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. AAAI Yuan Cao, Quanquan Gu
2020	J	`jnl`	Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2020	J	`jnl`	Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
2019	J	`jnl`	A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. CoRR Yuan Cao, Quanquan Gu
2019	A*	`conf`	Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. NeurIPS Spencer Frei, Yuan Cao, Quanquan Gu
2019	J	`jnl`	Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. CoRR Spencer Frei, Yuan Cao, Quanquan Gu
2019	A*	`conf`	Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. NeurIPS Yuan Cao, Quanquan Gu
2019	J	`jnl`	Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. CoRR Yuan Cao, Quanquan Gu
2019	J	`jnl`	How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
2019	A*	`conf`	Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. NeurIPS Yuan Cao, Quanquan Gu
2019	J	`jnl`	Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. CoRR Yuan Cao, Quanquan Gu
2019	J	`jnl`	Towards Understanding the Spectral Bias of Deep Learning. CoRR Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2018	J	`jnl`	On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2018	J	`jnl`	Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2018	A*	`conf`	The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. ICML Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang