The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. - Hao Lu; Yuan Cao 006; Junwei Lu; Han Liu 001; Zhaoran Wang 001 (ICML, 2018)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (CoRR, 2018)

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (CoRR, 2018)

Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. - Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. - Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. - Yuan Cao 006; Quanquan Gu (CoRR, 2019)

Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. - Yuan Cao 006; Quanquan Gu (AAAI, 2020)

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. - Dongruo Zhou; Yuan Cao 006; Quanquan Gu (AISTATS, 2020)

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. - Jinghui Chen; Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (IJCAI, 2020)

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (NeurIPS, 2020)

Agnostic Learning of a Single Neuron with Gradient Descent. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2020)

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (CoRR, 2020)

Gradient descent optimizes over-parameterized deep ReLU networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (Mach. Learn., 2020)

How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? - Zixiang Chen; Yuan Cao 006; Difan Zou; Quanquan Gu (ICLR, 2021)

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Towards Understanding the Spectral Bias of Deep Learning. - Yuan Cao 006; Zhiying Fang; Yue Wu; Ding-Xuan Zhou; Quanquan Gu (IJCAI, 2021)

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. - Yuan Cao 006; Quanquan Gu; Mikhail Belkin (NeurIPS, 2021)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Misha Belkin; Quanquan Gu (NeurIPS, 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Mikhail Belkin; Quanquan Gu (CoRR, 2022)

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. - Yuan Cao 006; Difan Zou; Yuanzhi Li; Quanquan Gu (COLT, 2023)

How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. - Yiwen Kou; Zixiang Chen; Yuan Cao 006; Quanquan Gu (ICLR, 2023)

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. - Difan Zou; Yuan Cao 006; Yuanzhi Li; Quanquan Gu (ICLR, 2023)

Understanding Train-Validation Split in Meta-Learning with Neural Networks. - Xinzhe Zuo; Zixiang Chen; Huaxiu Yao; Yuan Cao 006; Quanquan Gu (ICLR, 2023)

The Benefits of Mixup for Feature Learning. - Difan Zou; Yuan Cao 006; Yuanzhi Li; Quanquan Gu (ICML, 2023)

Benign Overfitting in Adversarially Robust Linear Classification. - Jinghui Chen; Yuan Cao 006; Quanquan Gu (UAI, 2023)

Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (CoRR, 2023)

Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. - Xuran Meng; Difan Zou; Yuan Cao 006 (ICML, 2024)

On the Comparison between Multi-modal and Single-modal Contrastive Learning. - Wei Huang 034; Andi Han; Yongqiang Chen; Yuan Cao 006; Zhiqiang Xu 003; Taiji Suzuki (NeurIPS, 2024)

The Implicit Bias of Adam on Separable Data. - Chenyang Zhang; Difan Zou; Yuan Cao 006 (NeurIPS, 2024)

Initialization Matters: On the Benign Overfitting of Two-Layer ReLU CNN with Fully Trainable Layers. - Shuning Shang; Xuran Meng; Yuan Cao 006; Difan Zou (CoRR, 2024)

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods. - Yuanzhe Tao; Huizhuo Yuan; Xun Zhou; Yuan Cao 006; Quanquan Gu (CoRR, 2024)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Jinghui Chen; Yuan Cao 006; Ziyan Yang; Quanquan Gu (Trans. Mach. Learn. Res., 2024)

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (AISTATS, 2025)

On the Power of Multitask Representation Learning with Gradient Descent. - Qiaobo Li; Zixiang Chen; Yihe Deng; Yiwen Kou; Yuan Cao 006; Quanquan Gu (AISTATS, 2025)

On the Feature Learning in Diffusion Models. - Andi Han; Wei Huang 034; Yuan Cao 006; Difan Zou (ICLR, 2025)

Transformer Learns Optimal Variable Selection in Group-Sparse Classification. - Chenyang Zhang; Xuran Meng; Yuan Cao 006 (ICLR, 2025)

On the Robustness of Transformers against Context Hijacking for Linear Classification. - Tianle Li; Chenyang Zhang; Xingwu Chen; Yuan Cao 006; Difan Zou (CoRR, 2025)

Per-example gradient regularization improves learning signals from noisy data. - Xuran Meng; Yuan Cao 006; Difan Zou (Mach. Learn., 2025)