Zou, Difan

73 publications

ICLR 2025 Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability Yujin Han, Lei Xu, Sirui Chen, Difan Zou, Chaochao Lu

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

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

NeurIPS 2025 F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning Hangwei Zhang, KangChun, Yan Wang, Difan Zou

NeurIPS 2025 Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory Hanru Bai, Weiyang Ding, Difan Zou

ICLR 2025 How Does Critical Batch Size Scale in Pre-Training? Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean Foster, Sham M. Kakade

NeurIPS 2025 How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki

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

NeurIPS 2025 Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning Dechen Zhang, Zhenmei Shi, Yi Zhang, Yingyu Liang, Difan Zou

ICML 2025 Masked Autoencoders Are Effective Tokenizers for Diffusion Models Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang, Ze Wang, Zicheng Liu, Difan Zou, Bhiksha Raj

ICLR 2025 On the Feature Learning in Diffusion Models Andi Han, Wei Huang, Yuan Cao, Difan Zou

NeurIPS 2025 On the Robustness of Transformers Against Context Hijacking for Linear Classification Tianle Li, Chenyang Zhang, Xingwu Chen, Yuan Cao, Difan Zou

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

MLJ 2025 Per-Example Gradient Regularization Improves Learning Signals from Noisy Data Xuran Meng, Yuan Cao, Difan Zou

ICLRW 2025 SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution Chengxing Xie, Bowen Li, Chang Gao, He Du, Wai Lam, Difan Zou, Kai Chen

NeurIPS 2025 Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-Image Generation Yao Teng, Fu-Yun Wang, Xian Liu, Zhekai Chen, Han Shi, Yu Wang, Zhenguo Li, Weiyang Liu, Difan Zou, Xihui Liu

ICML 2025 Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou

ICLRW 2025 Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou

NeurIPS 2025 Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks Xuan Tang, Han Zhang, Yuan Cao, Difan Zou

ICMLW 2024 A Human-like Reasoning Framework for Multi-Phases Planning Task with Large Language Models Chengxing Xie, Difan Zou

NeurIPS 2024 An In-Depth Investigation of Sparse Rate Reduction in Transformer-like Models Yunzhe Hu, Difan Zou, Dong Xu

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

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

COLT 2024 Faster Sampling Without Isoperimetry via Diffusion-Based Monte Carlo Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang

ICML 2024 Faster Sampling via Stochastic Gradient Proximal Sampler Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang

ICMLW 2024 Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks Chenyang Zhang, Gao Peifeng, Difan Zou, Yuan Cao

NeurIPSW 2024 How Does Critical Batch Size Scale in Pre-Training? Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean Foster, Sham M. Kakade

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 Bartlett

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

ICMLW 2024 How Transformers Utilize Multi-Head Attention in In-Context Learning? a Case Study on Sparse Linear Regression Xingwu Chen, Lei Zhao, Difan Zou

ICML 2024 Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference Yujin Han, Difan Zou

NeurIPSW 2024 On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models Yi Zhang, Difan Zou

NeurIPSW 2024 On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models Yi Zhang, Difan Zou

ICMLW 2024 On the Discrepancy and Connection Between Memorization and Generation in Diffusion Models Hanyu Wang, Yujin Han, Difan Zou

CoLLAs 2024 On the Limitation and Experience Replay for GNNs in Continual Learning Junwei Su, Difan Zou, Chuan Wu

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

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

ICMLW 2024 Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yian Ma, Tong Zhang

ICMLW 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

ICMLW 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

NeurIPS 2024 Slight Corruption in Pre-Training Data Makes Better Diffusion Models Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj

NeurIPS 2024 The Implicit Bias of Adam on Separable Data Chenyang Zhang, Difan Zou, Yuan Cao

ICMLW 2024 The Implicit Bias of Adam on Separable Data Chenyang Zhang, Difan Zou, Yuan Cao

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

NeurIPSW 2023 Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou

JMLR 2023 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

NeurIPSW 2023 DISK: Domain Inference for Discovering Spurious Correlation with KL-Divergence Yujin Han, Difan Zou

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, Yuanzhi Li, Quanquan Gu

COLT 2023 The Implicit Bias of Batch Normalization in Linear Models and Two-Layer Linear Convolutional Neural Networks Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu

ICML 2023 Towards Robust Graph Incremental Learning on Evolving Graphs Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu

ICLR 2023 Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu

AISTATS 2022 Self-Training Converts Weak Learners to Strong Learners in Mixture Models Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu

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

NeurIPS 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression Under Covariate Shift Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade

COLT 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade

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

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

ICLR 2021 How Much Over-Parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu

ICML 2021 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients Difan Zou, Quanquan Gu

ICML 2021 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise Difan Zou, Spencer Frei, Quanquan Gu

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

MLJ 2020 Gradient Descent Optimizes Over-Parameterized Deep ReLU Networks Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

ICLR 2020 Improving Adversarial Robustness Requires Revisiting Misclassified Examples Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu

ICLR 2020 On the Global Convergence of Training Deep Linear ResNets Difan Zou, Philip M. Long, 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, Yizhou Sun, Quanquan Gu

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

NeurIPS 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction Difan Zou, Pan Xu, Quanquan Gu

NeurIPS 2018 Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu

ICML 2018 Stochastic Variance-Reduced Hamilton Monte Carlo Methods Difan Zou, Pan Xu, Quanquan Gu

UAI 2018 Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics Difan Zou, Pan Xu, Quanquan Gu