Difan Zou

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 85

Top Topics

Monte Carlo Methods

Diffusion Models

Top Venues

Publications

Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
Slight Corruption in Pre-training Data Makes Better Diffusion Models. CoRR (2024)
Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang
An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. CoRR (2024)
Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou
Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate. ICLR (2024)
Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR (2024)
Min Cai, Yuchen Zhang, Shichang Zhang, Fan Yin, Difan Zou, Yisong Yue, Ziniu Hu
Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller. CoRR (2024)
Yujin Han, Difan Zou
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference. CoRR (2024)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR (2024)
Junwei Su, Difan Zou, Chuan Wu
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. ICLR (2024)
Junwei Su, Difan Zou, Chuan Wu
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. CoRR (2024)
Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT (2024)
Kun Zhai, Yifeng Gao, Xingjun Ma, Difan Zou, Guangnan Ye, Yu-Gang Jiang
The Dog Walking Theory: Rethinking Convergence in Federated Learning. CoRR (2024)
Chenyang Zhang, Difan Zou, Yuan Cao
The Implicit Bias of Adam on Separable Data. CoRR (2024)
Xingwu Chen, Difan Zou
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks. CoRR (2024)
Yifan Hao, Yong Lin, Difan Zou, Tong Zhang
On the Benefits of Over-parameterization for Out-of-Distribution Generalization. CoRR (2024)
Chengxing Xie, Difan Zou
A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models. CoRR (2024)
Junwei Su, Difan Zou, Chuan Wu
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems. CoRR (2024)
Yunhao Chen, Xingjun Ma, Difan Zou, Yu-Gang Jiang
Extracting Training Data from Unconditional Diffusion Models. CoRR (2024)
Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. CoRR (2024)
Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
Towards Robust Graph Incremental Learning on Evolving Graphs. CoRR (2024)
Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR (2024)
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
The Benefits of Mixup for Feature Learning. ICML (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR (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)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR (2023)
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)
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 (2023)
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
The Benefits of Mixup for Feature Learning. CoRR (2023)
Xuran Meng, Yuan Cao, Difan Zou
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data. CoRR (2023)
Xu Luo, Difan Zou, Lianli Gao, Zenglin Xu, Jingkuan Song
Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks. CoRR (2023)
Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
Towards Robust Graph Incremental Learning on Evolving Graphs. ICML (2023)
Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou
Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates. CoRR (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML (2023)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS (2022)
Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
Self-training Converts Weak Learners to Strong Learners in Mixture Models. AISTATS (2022)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR (2022)
Hong Qi, Difan Zou, Chen Gong, Zhengyuan Xu
Two-Dimensional Intensity Distribution and Adaptive Power Allocation for Ultraviolet Ad-Hoc Network. IEEE Trans. Green Commun. Netw. 6 (1) (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS (2022)
Difan Zou, Quanquan Gu
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. ICML (2021)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR (2021)
Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
Self-training Converts Weak Learners to Strong Learners in Mixture Models. CoRR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR (2021)
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR (2021)
Difan Zou, Spencer Frei, Quanquan Gu
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. ICML (2021)
Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. SIAM J. Sci. Comput. 43 (1) (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT (2021)
Difan Zou, Pan Xu, Quanquan Gu
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI (2021)
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS (2021)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR (2021)
Difan Zou, Spencer Frei, Quanquan Gu
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. CoRR (2021)
Difan Zou, Philip M. Long, Quanquan Gu
On the Global Convergence of Training Deep Linear ResNets. ICLR (2020)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR (2020)
Hong Qi, Difan Zou, Chen Gong, Zhengyuan Xu
Two-dimensional Intensity Distribution and Connectivity in Ultraviolet Ad-Hoc Network. ICC (2020)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109 (3) (2020)
Difan Zou, Philip M. Long, Quanquan Gu
On the Global Convergence of Training Deep Linear ResNets. CoRR (2020)
Difan Zou, Pan Xu, Quanquan Gu
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR (2020)
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. ICLR (2020)
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS (2019)
Difan Zou, Quanquan Gu
An Improved Analysis of Training Over-parameterized Deep Neural Networks. NeurIPS (2019)
Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. CoRR (2019)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS (2019)
Difan Zou, Pan Xu, Quanquan Gu
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS (2019)
Difan Zou, Quanquan Gu
An Improved Analysis of Training Over-parameterized Deep Neural Networks. CoRR (2019)
Xiaona Liu, Chen Gong, Difan Zou, Zunaira Babar, Zhengyuan Xu, Lajos Hanzo
Signal Characterization and Achievable Transmission Rate of VLC Under Receiver Nonlinearity. IEEE Access 7 (2019)
Difan Zou, Chen Gong, Kun Wang, Zhengyuan Xu
Characterization on Practical Photon Counting Receiver in Optical Scattering Communication. IEEE Trans. Commun. 67 (3) (2019)
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. CoRR (2019)
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR (2019)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR (2018)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML (2018)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR (2018)
Difan Zou, Chen Gong, Zhengyuan Xu
Secrecy Rate of MISO Optical Wireless Scattering Communications. IEEE Trans. Commun. 66 (1) (2018)
Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS (2018)
Difan Zou, Chen Gong, Zhengyuan Xu
Signal Detection Under Short-Interval Sampling of Continuous Waveforms for Optical Wireless Scattering Communication. IEEE Trans. Wirel. Commun. 17 (5) (2018)
Difan Zou, Pan Xu, Quanquan Gu
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI (2018)
Difan Zou, Chen Gong, Kun Wang, Zhengyuan Xu
Characterization of a Practical Photon Counting Receiver in Optical Scattering Communication. GLOBECOM (2017)
Difan Zou, Chen Gong, Zhengyuan Xu
Analysis on Practical Photon Counting Receiver in Optical Scattering Communication. CoRR (2017)
Yaodong Yu, Difan Zou, Quanquan Gu
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR (2017)
Kun Wang, Chen Gong, Difan Zou, Zhengyuan Xu
Turbulence channel modeling and non-parametric estimation for optical wireless scattering communication. ICCS (2016)
Difan Zou, Chen Gong, Zhengyuan Xu
Optical wireless scattering communication system with a non-ideal photon-counting receiver. GlobalSIP (2016)
Difan Zou, Zhengyuan Xu, Chen Gong
Performance of non-line-of-sight ultraviolet scattering communication under different altitudes. ICCC (2016)
Difan Zou, Chen Gong, Zhengyuan Xu
Signal Detection under Short-Interval Sampling of Continuous Waveforms for Optical Wireless Scattering Communication. CoRR (2016)
Difan Zou, Shang-Bin Li, Zhengyuan Xu
Improving the NLOS optical scattering channel via beam reshaping. ACSSC (2014)