Difan Zou - RankMe

158 papers A* 45A 4B 1C 1Misc 2Journal 99Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bidirectional Joint State-Memory Deep Bayesian Smoother for Motion Estimation. IEEE Trans. Aerosp. Electron. Syst. Shi Yan, Yan Liang, Huayu Zhang, Le Zheng, Difan Zou, Binglu Wang
2026	J	`jnl`	Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction. CoRR Xingwu Chen, Zhanqiu Zhang, Yiwen Guo, Difan Zou
2026	J	`jnl`	Complexity-Aware Policy via Heterogeneous Experts for Robotic Manipulation. IEEE Trans. Circuits Syst. Video Technol. Ming Yang, Yunzhe Hu, Ge Yuan, Difan Zou, Jianxin Pang, Dong Xu
2026	J	`jnl`	DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders. CoRR Xu Wang, Bingqing Jiang, Yu Wan, Baosong Yang, Lingpeng Kong, Difan Zou
2026	A*	`conf`	Learning Diffusion Policy from Primitive Skills for Robot Manipulation. AAAI Zhihao Gu, Ming Yang, Difan Zou, Dong Xu
2026	J	`jnl`	Learning Diffusion Policy from Primitive Skills for Robot Manipulation. CoRR Zhihao Gu, Ming Yang, Difan Zou, Dong Xu
2026	J	`jnl`	ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance. CoRR Zichao Yu, Ming Li, Wenyi Zhang, Difan Zou, Weiguo Gao
2026	A*	`conf`	SIDE: Surrogate Conditional Data Extraction from Diffusion Models. AAAI Yunhao Chen, Shujie Wang, Difan Zou, Xingjun Ma
2026	J	`jnl`	Scaling Laws for Precision in High-Dimensional Linear Regression. CoRR Dechen Zhang, Xuan Tang, Yingyu Liang, Difan Zou
2026	J	`jnl`	The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient. CoRR Jichu Li, Xuan Tang, Difan Zou
2025	J	`jnl`	A Convergence Analysis of Adaptive Optimizers under Floating-point Quantization. CoRR Xuan Tang, Jichu Li, Difan Zou
2025	J	`jnl`	A Random Matrix Analysis of In-context Memorization for Nonlinear Attention. CoRR Zhenyu Liao, Jiaqing Liu, Tianqi Hou, Difan Zou, Zenan Ling
2025	J	`jnl`	Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion. CoRR Xunpeng Huang, Yingyu Lin, Nikki Lijing Kuang, Hanze Dong, Difan Zou, Yian Ma, Tong Zhang
2025	A*	`conf`	Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension ability. ICLR Yujin Han, Lei Xu, Sirui Chen, Difan Zou, Chaochao Lu
2025	A*	`conf`	Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? ICML Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou
2025	J	`jnl`	Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? CoRR Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou
2025	J	`jnl`	Capturing Conditional Dependence via Auto-regressive Diffusion Models. CoRR Xunpeng Huang, Yujin Han, Difan Zou, Yian Ma, Tong Zhang
2025	J	`jnl`	Differentiable Evolutionary Reinforcement Learning. CoRR Sitao Cheng, Tianle Li, Xuhan Huang, Xunjian Yin, Difan Zou
2025	J	`jnl`	Does higher interpretability imply better utility? A Pairwise Analysis on Sparse Autoencoders. CoRR Xu Wang, Yan Hu, Benyou Wang, Difan Zou
2025	J	`jnl`	F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning. CoRR Hangwei Zhang, Chun Kang, Yan Wang, Difan Zou
2025	J	`jnl`	Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks. CoRR Chenyang Zhang, Peifeng Gao, Difan Zou, Yuan Cao
2025	J	`jnl`	Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory. CoRR Hanru Bai, Weiyang Ding, Difan Zou
2025	A*	`conf`	How Does Critical Batch Size Scale in Pre-training? ICLR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2025	J	`jnl`	How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? CoRR Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki
2025	A*	`conf`	HyPoGen: Optimization-Biased Hypernetworks for Generalizable Policy Generation. ICLR Hanxiang Ren, Li Sun, Xulong Wang, Pei Zhou, Zewen Wu, Siyan Dong, Difan Zou, Youyi Zheng, Yanchao Yang
2025	J	`jnl`	Hyperspherical Energy Transformer with Recurrent Depth. CoRR Yunzhe Hu, Difan Zou, Dong Xu
2025	J	`jnl`	Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning. CoRR Dechen Zhang, Zhenmei Shi, Yi Zhang, Yingyu Liang, Difan Zou
2025	J	`jnl`	Learning under Quantization for High-Dimensional Linear Regression. CoRR Dechen Zhang, Junwei Su, Difan Zou
2025	A*	`conf`	Masked Autoencoders Are Effective Tokenizers for Diffusion Models. ICML Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang, Ze Wang, Zicheng Liu, Difan Zou, Bhiksha Raj
2025	J	`jnl`	Masked Autoencoders Are Effective Tokenizers for Diffusion Models. CoRR Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang, Ze Wang, Zicheng Liu, Difan Zou, Bhiksha Raj
2025	A*	`conf`	Model Unlearning via Sparse Autoencoder Subspace Guided Projections. EMNLP Xu Wang, Zihao Li, Benyou Wang, Yan Hu, Difan Zou
2025	J	`jnl`	Model Unlearning via Sparse Autoencoder Subspace Guided Projections. CoRR Xu Wang, Zihao Li, Benyou Wang, Yan Hu, Difan Zou
2025	J	`jnl`	On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models. CoRR Yi Zhang, Zhenyu Liao, Jingfeng Wu, Difan Zou
2025	J	`jnl`	On the Complexity Theory of Masked Discrete Diffusion: From poly(1/ε) to Nearly ε-Free. CoRR Xunpeng Huang, Yingyu Lin, Nishant Jain, Kaibo Wang, Difan Zou, Yian Ma, Tong Zhang
2025	A*	`conf`	On the Feature Learning in Diffusion Models. ICLR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2025	J	`jnl`	On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models. CoRR Xingwu Chen, Tianle Li, Difan Zou
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	A*	`conf`	Parallelized Autoregressive Visual Generation. CVPR Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, Xihui Liu
2025	J	`jnl`	Per-example gradient regularization improves learning signals from noisy data. Mach. Learn. Xuran Meng, Yuan Cao, Difan Zou
2025	J	`jnl`	Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation. CoRR Yi Zhang, Difan Zou
2025	J	`jnl`	Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance. CoRR Yujie Wei, Shiwei Zhang, Hangjie Yuan, Yujin Han, Zhekai Chen, Jiayu Wang, Difan Zou, Xihui Liu, Yingya Zhang, Yu Liu, Hongming Shan
2025	J	`jnl`	STGAN: Spatial-Temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction. IEEE Trans. Intell. Transp. Syst. Shilin Tong, Difei Wu, Xiaona Liu, Le Zheng, Yuchuan Du, Difan Zou
2025	J	`jnl`	STGAN: Spatial-temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction. CoRR Shilin Tong, Difei Wu, Xiaona Liu, Le Zheng, Yuchuan Du, Difan Zou
2025	—	`conf`	SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution. ACL (Findings) Chengxing Xie, Bowen Li, Chang Gao, He Du, Wai Lam, Difan Zou, Kai Chen
2025	J	`jnl`	SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution. CoRR Chengxing Xie, Bowen Li, Chang Gao, He Du, Wai Lam, Difan Zou, Kai Chen
2025	J	`jnl`	Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs. CoRR Yujin Han, Hao Chen, Andi Han, Zhiheng Wang, Xinyu Lin, Yingya Zhang, Shiwei Zhang, Difan Zou
2025	J	`jnl`	Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation. CoRR Yao Teng, Fuyun Wang, Xian Liu, Zhekai Chen, Han Shi, Yu Wang, Zhenguo Li, Weiyang Liu, Difan Zou, Xihui Liu
2025	J	`jnl`	Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression. CoRR Xingwu Chen, Miao Lu, Beining Wu, Difan Zou
2025	A*	`conf`	Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis. ICML Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou
2025	J	`jnl`	Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis. CoRR Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou
2025	J	`jnl`	Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks. CoRR Xuan Tang, Han Zhang, Yuan Cao, Difan Zou
2024	J	`jnl`	A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models. CoRR Chengxing Xie, Difan Zou
2024	J	`jnl`	An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. CoRR Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang
2024	A*	`conf`	An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models. NeurIPS Yunzhe Hu, Difan Zou, Dong Xu
2024	J	`jnl`	An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models. CoRR Yunzhe Hu, Difan Zou, Dong Xu
2024	A*	`conf`	Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate. ICLR Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou
2024	A*	`conf`	Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data. ICML Xuran Meng, Difan Zou, Yuan Cao
2024	J	`jnl`	Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability. CoRR Yujin Han, Lei Xu, Sirui Chen, Difan Zou, Chaochao Lu
2024	J	`jnl`	Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. 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, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2024	A*	`conf`	Faster Sampling via Stochastic Gradient Proximal Sampler. ICML Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang
2024	J	`jnl`	Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
2024	A*	`conf`	Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang
2024	J	`jnl`	Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. CoRR Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang
2024	J	`jnl`	How Does Critical Batch Size Scale in Pre-training? CoRR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2024	A*	`conf`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2024	A*	`conf`	How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression. NeurIPS Xingwu Chen, Lei Zhao, Difan Zou
2024	J	`jnl`	How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression. CoRR Xingwu Chen, Lei Zhao, Difan Zou
2024	A*	`conf`	Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference. ICML Yujin Han, Difan Zou
2024	J	`jnl`	Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference. CoRR Yujin Han, Difan Zou
2024	J	`jnl`	Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems. CoRR Junwei Su, Difan Zou, Chuan Wu
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	J	`jnl`	On the Benefits of Over-parameterization for Out-of-Distribution Generalization. CoRR Yifan Hao, Yong Lin, Difan Zou, Tong Zhang
2024	J	`jnl`	On the Feature Learning in Diffusion Models. CoRR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2024	—	`conf`	On the Limitation and Experience Replay for GNNs in Continual Learning. CoLLAs Junwei Su, Difan Zou, Chuan Wu
2024	—	`conf`	Optimized Transmit Beamformers for Dual-Function RadCom System. GLOBECOM (Workshops) Junhui Qian, Zhuoran Sun, Jie Wang, Le Zheng, Difan Zou, Xianxiang Yu, Jing Yang
2024	A*	`conf`	PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. ICLR Junwei Su, Difan Zou, Chuan Wu
2024	J	`jnl`	PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. CoRR Junwei Su, Difan Zou, Chuan Wu
2024	J	`jnl`	Parallelized Autoregressive Visual Generation. CoRR Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, Xihui Liu
2024	A*	`conf`	Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. NeurIPS Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yian Ma, Tong Zhang
2024	J	`jnl`	Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang
2024	J	`jnl`	Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller. CoRR Min Cai, Yuchen Zhang, Shichang Zhang, Fan Yin, Difan Zou, Yisong Yue, Ziniu Hu
2024	A*	`conf`	Slight Corruption in Pre-training Data Makes Better Diffusion Models. NeurIPS Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
2024	J	`jnl`	Slight Corruption in Pre-training Data Makes Better Diffusion Models. CoRR Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
2024	J	`jnl`	The Dog Walking Theory: Rethinking Convergence in Federated Learning. CoRR Kun Zhai, Yifeng Gao, Xingjun Ma, Difan Zou, Guangnan Ye, Yu-Gang Jiang
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 Robust Graph Incremental Learning on Evolving Graphs. CoRR Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
2024	J	`jnl`	Towards a Theoretical Understanding of Memorization in Diffusion Models. CoRR Yunhao Chen, Xingjun Ma, Difan Zou, Yu-Gang Jiang
2024	A*	`conf`	What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks. ICML Xingwu Chen, Difan Zou
2024	J	`jnl`	What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks. CoRR Xingwu Chen, Difan Zou
2023	J	`jnl`	Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates. CoRR Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou
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`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. J. Mach. Learn. Res. Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	A*	`conf`	Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	J	`jnl`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2023	J	`jnl`	Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	J	`jnl`	Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks. CoRR Xu Luo, Difan Zou, Lianli Gao, Zenglin Xu, Jingkuan Song
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`	Towards Robust Graph Incremental Learning on Evolving Graphs. ICML Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
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`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A*	`conf`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A	`conf`	Self-training Converts Weak Learners to Strong Learners in Mixture Models. AISTATS Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
2022	A*	`conf`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	Two-Dimensional Intensity Distribution and Adaptive Power Allocation for Ultraviolet Ad-Hoc Network. IEEE Trans. Green Commun. Netw. Hong Qi, Difan Zou, Chen Gong, Zhengyuan Xu
2022	—	`—`	Understanding the Role of Optimization Algorithms in Learning Over-parameterized Models Difan Zou
2021	A*	`conf`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	J	`jnl`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	A*	`conf`	Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2021	A	`conf`	Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI Difan Zou, Pan Xu, 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	J	`jnl`	Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. SIAM J. Sci. Comput. Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
2021	J	`jnl`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	A*	`conf`	On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. ICML Difan Zou, Quanquan Gu
2021	A*	`conf`	Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. ICML Difan Zou, Spencer Frei, Quanquan Gu
2021	J	`jnl`	Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. CoRR Difan Zou, Spencer Frei, Quanquan Gu
2021	J	`jnl`	Self-training Converts Weak Learners to Strong Learners in Mixture Models. CoRR Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
2021	A*	`conf`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021	J	`jnl`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
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	J	`jnl`	Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2020	J	`jnl`	Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR Difan Zou, Pan Xu, Quanquan Gu
2020	J	`jnl`	Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2020	A*	`conf`	Improving Adversarial Robustness Requires Revisiting Misclassified Examples. ICLR Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
2020	A*	`conf`	On the Global Convergence of Training Deep Linear ResNets. ICLR Difan Zou, Philip M. Long, Quanquan Gu
2020	J	`jnl`	On the Global Convergence of Training Deep Linear ResNets. CoRR Difan Zou, Philip M. Long, Quanquan Gu
2020	—	`conf`	Two-dimensional Intensity Distribution and Connectivity in Ultraviolet Ad-Hoc Network. ICC Hong Qi, Difan Zou, Chen Gong, Zhengyuan Xu
2019	A*	`conf`	An Improved Analysis of Training Over-parameterized Deep Neural Networks. NeurIPS Difan Zou, Quanquan Gu
2019	J	`jnl`	An Improved Analysis of Training Over-parameterized Deep Neural Networks. CoRR Difan Zou, Quanquan Gu
2019	J	`jnl`	Characterization on Practical Photon Counting Receiver in Optical Scattering Communication. IEEE Trans. Commun. Difan Zou, Chen Gong, Kun Wang, Zhengyuan Xu
2019	J	`jnl`	How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
2019	J	`jnl`	Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. CoRR Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
2019	A*	`conf`	Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
2019	J	`jnl`	Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. CoRR Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
2019	A	`conf`	Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS Difan Zou, Pan Xu, Quanquan Gu
2019	J	`jnl`	Signal Characterization and Achievable Transmission Rate of VLC Under Receiver Nonlinearity. IEEE Access Xiaona Liu, Chen Gong, Difan Zou, Zunaira Babar, Zhengyuan Xu, Lajos Hanzo
2019	A*	`conf`	Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS Difan Zou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
2018	J	`jnl`	Secrecy Rate of MISO Optical Wireless Scattering Communications. IEEE Trans. Commun. Difan Zou, Chen Gong, Zhengyuan Xu
2018	J	`jnl`	Signal Detection Under Short-Interval Sampling of Continuous Waveforms for Optical Wireless Scattering Communication. IEEE Trans. Wirel. Commun. Difan Zou, Chen Gong, Zhengyuan Xu
2018	J	`jnl`	Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2018	A*	`conf`	Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML Difan Zou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR Difan Zou, Pan Xu, Quanquan Gu
2018	A	`conf`	Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI Difan Zou, Pan Xu, Quanquan Gu
2017	J	`jnl`	Analysis on Practical Photon Counting Receiver in Optical Scattering Communication. CoRR Difan Zou, Chen Gong, Zhengyuan Xu
2017	B	`conf`	Characterization of a Practical Photon Counting Receiver in Optical Scattering Communication. GLOBECOM Difan Zou, Chen Gong, Kun Wang, Zhengyuan Xu
2017	J	`jnl`	Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR Yaodong Yu, Difan Zou, Quanquan Gu
2016	—	`conf`	Optical wireless scattering communication system with a non-ideal photon-counting receiver. GlobalSIP Difan Zou, Chen Gong, Zhengyuan Xu
2016	C	`conf`	Performance of non-line-of-sight ultraviolet scattering communication under different altitudes. ICCC Difan Zou, Zhengyuan Xu, Chen Gong
2016	J	`jnl`	Signal Detection under Short-Interval Sampling of Continuous Waveforms for Optical Wireless Scattering Communication. CoRR Difan Zou, Chen Gong, Zhengyuan Xu
2016	Misc	`conf`	Turbulence channel modeling and non-parametric estimation for optical wireless scattering communication. ICCS Kun Wang, Chen Gong, Difan Zou, Zhengyuan Xu
2014	Misc	`conf`	Improving the NLOS optical scattering channel via beam reshaping. ACSSC Difan Zou, Shang-Bin Li, Zhengyuan Xu