Improving the NLOS optical scattering channel via beam reshaping. - Difan Zou; Shang-Bin Li; Zhengyuan Xu (ACSSC, 2014)

Optical wireless scattering communication system with a non-ideal photon-counting receiver. - Difan Zou; Chen Gong 001; Zhengyuan Xu (GlobalSIP, 2016)

Performance of non-line-of-sight ultraviolet scattering communication under different altitudes. - Difan Zou; Zhengyuan Xu; Chen Gong 001 (ICCC, 2016)

Turbulence channel modeling and non-parametric estimation for optical wireless scattering communication. - Kun Wang 008; Chen Gong 001; Difan Zou; Zhengyuan Xu (ICCS, 2016)

Analysis on Practical Photon Counting Receiver in Optical Scattering Communication. - Difan Zou; Chen Gong 001; Zhengyuan Xu (CoRR, 2017)

Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. - Yaodong Yu; Difan Zou; Quanquan Gu (CoRR, 2017)

Stochastic Variance-Reduced Hamilton Monte Carlo Methods. - Difan Zou; Pan Xu 002; Quanquan Gu (ICML, 2018)

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. - Pan Xu 002; Jinghui Chen; Difan Zou; Quanquan Gu (NeurIPS, 2018)

Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. - Difan Zou; Pan Xu 002; Quanquan Gu (UAI, 2018)

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

Secrecy Rate of MISO Optical Wireless Scattering Communications. - Difan Zou; Chen Gong 001; Zhengyuan Xu (IEEE Trans. Commun., 2018)

Signal Detection Under Short-Interval Sampling of Continuous Waveforms for Optical Wireless Scattering Communication. - Difan Zou; Chen Gong 001; Zhengyuan Xu (IEEE Trans. Wirel. Commun., 2018)

Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. - Difan Zou; Pan Xu 002; Quanquan Gu (AISTATS, 2019)

Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. - Difan Zou; Pan Xu 002; Quanquan Gu (NeurIPS, 2019)

An Improved Analysis of Training Over-parameterized Deep Neural Networks. - Difan Zou; Quanquan Gu (NeurIPS, 2019)

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. - Difan Zou; Ziniu Hu; Yewen Wang; Song Jiang 002; Yizhou Sun; Quanquan Gu (NeurIPS, 2019)

Signal Characterization and Achievable Transmission Rate of VLC Under Receiver Nonlinearity. - Xiaona Liu; Chen Gong 001; Difan Zou; Zunaira Babar; Zhengyuan Xu; Lajos Hanzo (IEEE Access, 2019)

Characterization on Practical Photon Counting Receiver in Optical Scattering Communication. - Difan Zou; Chen Gong 001; Kun Wang 008; Zhengyuan Xu (IEEE Trans. Commun., 2019)

Two-dimensional Intensity Distribution and Connectivity in Ultraviolet Ad-Hoc Network. - Hong Qi; Difan Zou; Chen Gong 001; Zhengyuan Xu (ICC, 2020)

Improving Adversarial Robustness Requires Revisiting Misclassified Examples. - Yisen Wang 001; Difan Zou; Jinfeng Yi; James Bailey 001; Xingjun Ma; Quanquan Gu (ICLR, 2020)

On the Global Convergence of Training Deep Linear ResNets. - Difan Zou; Philip M. Long; Quanquan Gu (ICLR, 2020)

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu (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)

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu (ICLR, 2021)

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. - Difan Zou; Spencer Frei; Quanquan Gu (ICML, 2021)

On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. - Difan Zou; Quanquan Gu (ICML, 2021)

The Benefits of Implicit Regularization from SGD in Least Squares Problems. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Dean P. Foster; Sham M. Kakade (NeurIPS, 2021)

Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. - Difan Zou; Pan Xu 002; Quanquan Gu (UAI, 2021)

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. - Bao Wang 001; Difan Zou; Quanquan Gu; Stanley J. Osher (SIAM J. Sci. Comput., 2021)

Self-training Converts Weak Learners to Strong Learners in Mixture Models. - Spencer Frei; Difan Zou; Zixiang Chen; Quanquan Gu (AISTATS, 2022)

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2022)

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

Two-Dimensional Intensity Distribution and Adaptive Power Allocation for Ultraviolet Ad-Hoc Network. - Hong Qi; Difan Zou; Chen Gong 001; Zhengyuan Xu (IEEE Trans. Green Commun. Netw., 2022)

Understanding the Role of Optimization Algorithms in Learning Over-parameterized Models - Difan Zou (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)

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

Towards Robust Graph Incremental Learning on Evolving Graphs. - Junwei Su; Difan Zou; Zijun Zhang; Chuan Wu 001 (ICML, 2023)

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2023)

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

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (CoRR, 2023)

Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks. - Xu Luo 003; Difan Zou; Lianli Gao; Zenglin Xu; Jingkuan Song (CoRR, 2023)

Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates. - Miao Lu; Beining Wu; Xiaodong Yang; Difan Zou (CoRR, 2023)

Benign Overfitting of Constant-Stepsize SGD for Linear Regression. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (J. Mach. Learn. Res., 2023)

On the Limitation and Experience Replay for GNNs in Continual Learning. - Junwei Su; Difan Zou; Chuan Wu 001 (CoLLAs, 2024)

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi-An Ma; Tong Zhang 001 (COLT, 2024)

Optimized Transmit Beamformers for Dual-Function RadCom System. - Junhui Qian; Zhuoran Sun; Jie Wang; Le Zheng; Difan Zou; Xianxiang Yu; Jing Yang 033 (GLOBECOM (Workshops), 2024)

Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate. - Miao Lu; Beining Wu; Xiaodong Yang; Difan Zou (ICLR, 2024)

PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. - Junwei Su; Difan Zou; Chuan Wu 001 (ICLR, 2024)

How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Peter L. Bartlett (ICLR, 2024)

What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks. - Xingwu Chen; Difan Zou (ICML, 2024)

Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference. - Yujin Han; Difan Zou (ICML, 2024)

Faster Sampling via Stochastic Gradient Proximal Sampler. - Xunpeng Huang; Difan Zou; Hanze Dong; Yian Ma; Tong Zhang 001 (ICML, 2024)

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

Slight Corruption in Pre-training Data Makes Better Diffusion Models. - Hao Chen 102; Yujin Han; Diganta Misra; Xiang Li 106; Kai Hu 010; Difan Zou; Masashi Sugiyama; Jindong Wang 001; Bhiksha Raj (NeurIPS, 2024)

How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression. - Xingwu Chen; Lei Zhao; Difan Zou (NeurIPS, 2024)

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models. - Yunzhe Hu; Difan Zou; Dong Xu (NeurIPS, 2024)

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi Zhang; Yian Ma; Tong Zhang 001 (NeurIPS, 2024)

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

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. - Xunpeng Huang; Hanze Dong; Difan Zou; Tong Zhang 001 (CoRR, 2024)

Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems. - Junwei Su; Difan Zou; Chuan Wu 001 (CoRR, 2024)

On the Benefits of Over-parameterization for Out-of-Distribution Generalization. - Yifan Hao 002; Yong Lin; Difan Zou; Tong Zhang 001 (CoRR, 2024)

The Dog Walking Theory: Rethinking Convergence in Federated Learning. - Kun Zhai; Yifeng Gao; Xingjun Ma; Difan Zou; Guangnan Ye; Yu-Gang Jiang 001 (CoRR, 2024)

A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models. - Chengxing Xie; Difan Zou (CoRR, 2024)

Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller. - Min Cai; Yuchen Zhang; Shichang Zhang; Fan Yin; Difan Zou; Yisong Yue; Ziniu Hu (CoRR, 2024)

Towards a Theoretical Understanding of Memorization in Diffusion Models. - Yunhao Chen; Xingjun Ma; Difan Zou; Yu-Gang Jiang 001 (CoRR, 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)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution. - Chengxing Xie; Bowen Li 002; Chang Gao; He Du; Wai Lam; Difan Zou; Kai Chen 026 (ACL (Findings), 2025)

Parallelized Autoregressive Visual Generation. - Yuqing Wang; Shuhuai Ren; Zhijie Lin 001; Yujin Han; Haoyuan Guo; Zhenheng Yang; Difan Zou; Jiashi Feng; Xihui Liu (CVPR, 2025)

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

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension ability. - Yujin Han; Lei Xu; Sirui Chen; Difan Zou; Chaochao Lu (ICLR, 2025)

HyPoGen: Optimization-Biased Hypernetworks for Generalizable Policy Generation. - Hanxiang Ren; Li Sun; Xulong Wang; Pei Zhou; Zewen Wu; Siyan Dong; Difan Zou; Youyi Zheng; Yanchao Yang 001 (ICLR, 2025)

How Does Critical Batch Size Scale in Pre-training? - Hanlin Zhang 002; Depen Morwani; Nikhil Vyas 001; Jingfeng Wu; Difan Zou; Udaya Ghai; Dean P. Foster; Sham M. Kakade (ICLR, 2025)

Masked Autoencoders Are Effective Tokenizers for Diffusion Models. - Hao Chen; Yujin Han; Fangyi Chen; Xiang Li 001; Yidong Wang; Jindong Wang 001; Ze Wang 008; Zicheng Liu 001; Difan Zou; Bhiksha Raj (CoRR, 2025)

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? - Yujin Han; Andi Han; Wei Huang; Chaochao Lu; Difan Zou (CoRR, 2025)

Hyperspherical Energy Transformer with Recurrent Depth. - Yunzhe Hu; Difan Zou; Dong Xu (CoRR, 2025)

Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis. - Xu Wang 033; Yan Hu; Wenyu Du; Reynold Cheng; Benyou Wang; Difan Zou (CoRR, 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)

Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks. - Chenyang Zhang; Peifeng Gao; Difan Zou; Yuan Cao (CoRR, 2025)

Capturing Conditional Dependence via Auto-regressive Diffusion Models. - Xunpeng Huang; Yujin Han; Difan Zou; Yian Ma; Tong Zhang 001 (CoRR, 2025)

Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion. - Xunpeng Huang; Yingyu Lin; Nikki Lijing Kuang; Hanze Dong; Difan Zou; Yian Ma; Tong Zhang 001 (CoRR, 2025)

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation. - Yi Zhang; Difan Zou (CoRR, 2025)

Model Unlearning via Sparse Autoencoder Subspace Guided Projections. - Xu Wang 033; Zihao Li; Benyou Wang; Yan Hu; Difan Zou (CoRR, 2025)

On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models. - Xingwu Chen; Tianle Li; Difan Zou (CoRR, 2025)

A Random Matrix Analysis of In-context Memorization for Nonlinear Attention. - Zhenyu Liao; Jiaqing Liu; TianQi Hou; Difan Zou; Zenan Ling (CoRR, 2025)

Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs. - Yujin Han; Hao Chen; Andi Han; Zhiheng Wang; Xinyu Lin; Yingya Zhang; Shiwei Zhang; Difan Zou (CoRR, 2025)

STGAN: Spatial-Temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction. - Shilin Tong; Difei Wu; Xiaona Liu; Le Zheng; Yuchuan Du; Difan Zou (IEEE Trans. Intell. Transp. Syst., 2025)

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression. - Xingwu Chen; Miao Lu; Beining Wu; Difan Zou (CoRR, 2025)