A study of the VANET connectivity by percolation theory. - Xin Jin 008; Weijie Su 001; Yan Wei (CCNC, 2011)

Quantitative Analysis of the VANET Connectivity: Theory and Application. - Xin Jin 008; Weijie Su 001; Wei Yan 007 (VTC Spring, 2011)

SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax. - Emmanuel J. Candès; Weijie Su 001 (CoRR, 2015)

Private False Discovery Rate Control. - Cynthia Dwork; Weijie Su 001; Li Zhang 001 (CoRR, 2015)

False Discoveries Occur Early on the Lasso Path. - Weijie Su 001; Malgorzata Bogdan; Emmanuel J. Candès (CoRR, 2015)

A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights. - Weijie Su 001; Stephen P. Boyd; Emmanuel J. Candès (J. Mach. Learn. Res., 2016)

Acceleration via Symplectic Discretization of High-Resolution Differential Equations. - Bin Shi; Simon S. Du; Weijie J. Su; Michael I. Jordan (NeurIPS, 2019)

Gaussian Differential Privacy. - Jinshuo Dong; Aaron Roth 001; Weijie J. Su (CoRR, 2019)

Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks. - Qingyang Wu; He Li; Weijie Su 001; Lexin Li; Zhou Yu (CoRR, 2019)

Deep Learning with Gaussian Differential Privacy. - Zhiqi Bu; Jinshuo Dong; Qi Long; Weijie J. Su (CoRR, 2019)

The Local Elasticity of Neural Networks. - Hangfeng He 001; Weijie J. Su (ICLR, 2020)

Towards Understanding the Dynamics of the First-Order Adversaries. - Zhun Deng; Hangfeng He 001; Jiaoyang Huang; Weijie J. Su (ICML, 2020)

Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion. - Qinqing Zheng; Jinshuo Dong; Qi Long; Weijie J. Su (ICML, 2020)

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity. - Shuxiao Chen; Hangfeng He 001; Weijie J. Su (NeurIPS, 2020)

The Complete Lasso Tradeoff Diagram. - Hua Wang; Yachong Yang; Zhiqi Bu; Weijie J. Su (NeurIPS, 2020)

Benign Overfitting and Noisy Features. - Zhu Li; Weijie J. Su; Dino Sejdinovic (CoRR, 2020)

Sharp Bias-variance Tradeoffs of Hard Parameter Sharing in High-dimensional Linear Regression. - Hongyang R. Zhang; Fan Yang; Sen Wu 002; Weijie J. Su; Christopher Ré (CoRR, 2020)

Federated f-Differential Privacy. - Qinqing Zheng; Shuxiao Chen; Qi Long; Weijie J. Su (AISTATS, 2021)

Toward Better Generalization Bounds with Locally Elastic Stability. - Zhun Deng; Hangfeng He 001; Weijie J. Su (ICML, 2021)

Oneshot Differentially Private Top-k Selection. - Gang Qiao; Weijie J. Su; Li Zhang 001 (ICML, 2021)

A Central Limit Theorem for Differentially Private Query Answering. - Jinshuo Dong; Weijie J. Su; Linjun Zhang (NeurIPS, 2021)

You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism. - Weijie J. Su (NeurIPS, 2021)

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations. - Jiayao Zhang 001; Hua Wang; Weijie J. Su (NeurIPS, 2021)

Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks. - Cong Fang 001; Hangfeng He 001; Qi Long; Weijie J. Su (CoRR, 2021)

A Theorem of the Alternative for Personalized Federated Learning. - Shuxiao Chen; Qinqing Zheng; Qi Long; Weijie J. Su (CoRR, 2021)

Rejoinder: Gaussian Differential Privacy. - Jinshuo Dong; Aaron Roth 001; Weijie J. Su (CoRR, 2021)

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (CoRR, 2021)

On the Convergence of Deep Learning with Differential Privacy. - Zhiqi Bu; Hua Wang; Qi Long; Weijie J. Su (CoRR, 2021)

Neurashed: A Phenomenological Model for Imitating Deep Learning Training. - Weijie J. Su (CoRR, 2021)

Differentially private false discovery rate control. - Cynthia Dwork; Weijie J. Su; Li Zhang 001 (J. Priv. Confidentiality, 2021)

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (IEEE Trans. Inf. Theory, 2021)

Weighted Training for Cross-Task Learning. - Shuxiao Chen; Koby Crammer; Hangfeng He 001; Dan Roth 001; Weijie J. Su (ICLR, 2022)

An Unconstrained Layer-Peeled Perspective on Neural Collapse. - Wenlong Ji; Yiping Lu 001; Yiliang Zhang; Zhun Deng; Weijie J. Su (ICLR, 2022)

ROCK: Causal Inference Principles for Reasoning about Commonsense Causality. - Jiayao Zhang 001; Hongming Zhang 009; Weijie J. Su; Dan Roth 001 (ICML, 2022)

Causal Inference Principles for Reasoning about Commonsense Causality. - Jiayao Zhang 001; Hongming Zhang 009; Dan Roth 001; Weijie J. Su (CoRR, 2022)

Analytical Composition of Differential Privacy via the Edgeworth Accountant. - Hua Wang; Sheng Gao 003; Huanyu Zhang; Milan Shen; Weijie J. Su (CoRR, 2022)

A Truthful Owner-Assisted Scoring Mechanism. - Weijie J. Su (CoRR, 2022)

When does SGD favor flat minima? A quantitative characterization via linear stability. - Lei Wu; Mingze Wang; Weijie Su 001 (CoRR, 2022)

A Law of Data Separation in Deep Learning. - Hangfeng He 001; Weijie J. Su (CoRR, 2022)

Understanding the acceleration phenomenon via high-resolution differential equations. - Bin Shi; Simon S. Du; Michael I. Jordan; Weijie J. Su (Math. Program., 2022)

The Price of Competition: Effect Size Heterogeneity Matters in High Dimensions. - Hua Wang; Yachong Yang; Weijie J. Su (IEEE Trans. Inf. Theory, 2022)

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. - Zhun Deng; Jiayao Zhang 001; Linjun Zhang; Ting Ye; Yates Coley; Weijie J. Su; James Zou 001 (ICLR, 2023)

The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent. - Lei Wu; Weijie J. Su (ICML, 2023)

DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization. - Hua Wang; Sheng Gao 003; Huanyu Zhang; Weijie J. Su; Milan Shen (NeurIPS, 2023)

Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy. - Chendi Wang; Buxin Su; Jiayuan Ye 001; Reza Shokri; Weijie J. Su (NeurIPS, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

The Isotonic Mechanism for Exponential Family Estimation. - Yuling Yan; Weijie J. Su; Jianqing Fan (CoRR, 2023)

Reward Collapse in Aligning Large Language Models. - Ziang Song; Tianle Cai; Jason D. Lee; Weijie J. Su (CoRR, 2023)

DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework. - Hua Wang; Sheng Gao 003; Huanyu Zhang; Weijie J. Su; Milan Shen (CoRR, 2023)

An Isotonic Mechanism for Overlapping Ownership. - Jibang Wu; Haifeng Xu; Yifan Guo; Weijie Su 001 (CoRR, 2023)

What Should Data Science Education Do with Large Language Models? - Xinming Tu; James Zou 001; Weijie J. Su; Linjun Zhang (CoRR, 2023)

Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? - Shuxiao Chen; Qinqing Zheng; Qi Long; Weijie J. Su (J. Mach. Learn. Res., 2023)

On Learning Rates and Schrödinger Operators. - Bin Shi; Weijie Su 001; Michael I. Jordan (J. Mach. Learn. Res., 2023)

HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation. - Weijie J. Su; Yuancheng Zhu (J. Mach. Learn. Res., 2023)

On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks. - Yizhou Liu 001; Weijie J. Su; Tongyang Li (Quantum, 2023)

Eliciting Honest Information from Authors Using Sequential Review. - Yichi Zhang 003; Grant Schoenebeck; Weijie Su 001 (AAAI, 2024)

Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization. - Jiancong Xiao; Ruoyu Sun 001; Qi Long; Weijie Su 001 (COLT, 2024)

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners. - Bowen Jiang; Yangxinyu Xie; Zhuoqun Hao; Xiaomeng Wang 002; Tanwi Mallick; Weijie Su 001; Camillo J. Taylor; Dan Roth 001 (EMNLP, 2024)

Shifted Interpolation for Differential Privacy. - Jinho Bok; Weijie J. Su; Jason M. Altschuler (ICML, 2024)

Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning. - Chendi Wang; Yuqing Zhu 005; Weijie J. Su; Yu-Xiang Wang 003 (ICML, 2024)

Envisioning future deep learning theories: some basic concepts and characteristics. - Weijie J. Su (Sci. China Inf. Sci., 2024)

Can AI Be as Creative as Humans? - Haonan Wang; James Zou 001; Michael Mozer; Anirudh Goyal; Alex Lamb; Linjun Zhang; Weijie J. Su; Zhun Deng; Michael Qizhe Xie; Hannah Brown; Kenji Kawaguchi (CoRR, 2024)

WildfireGPT: Tailored Large Language Model for Wildfire Analysis. - Yangxinyu Xie; Tanwi Mallick; Joshua David Bergerson; John K. Hutchison; Duane R. Verner; Jordan Branham; M. Ross Alexander; Robert B. Ross; Yan Feng; Leslie-Anne Levy; Weijie J. Su (CoRR, 2024)

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. - Huiying Zhong; Zhun Deng; Weijie J. Su; Zhiwei Steven Wu; Linjun Zhang (CoRR, 2024)

A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules. - Xiang Li; Feng Ruan; Huiyuan Wang; Qi Long; Weijie J. Su (CoRR, 2024)

An Economic Solution to Copyright Challenges of Generative AI. - Jiachen T. Wang; Zhun Deng; Hiroaki Chiba-Okabe; Boaz Barak; Weijie J. Su (CoRR, 2024)

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization. - Jiancong Xiao; Ziniu Li; Xingyu Xie; Emily J. Getzen; Cong Fang 001; Qi Long; Weijie J. Su (CoRR, 2024)

AI Risk Management Should Incorporate Both Safety and Security. - Xiangyu Qi; Yangsibo Huang; Yi Zeng 005; Edoardo Debenedetti; Jonas Geiping; Luxi He; Kaixuan Huang; Udari Madhushani; Vikash Sehwag; Weijia Shi; Boyi Wei; Tinghao Xie; et al. (CoRR, 2024)

Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey. - Bowen Jiang; Yangxinyu Xie; Xiaomeng Wang 002; Weijie J. Su; Camillo J. Taylor; Tanwi Mallick (CoRR, 2024)

Tackling GenAI Copyright Issues: Originality Estimation and Genericization. - Hiroaki Chiba-Okabe; Weijie J. Su (CoRR, 2024)

Fine-Tuning Linear Layers Only Is a Simple yet Effective Way for Task Arithmetic. - Ruochen Jin; Bojian Hou; Jiancong Xiao; Weijie J. Su; Li Shen 001 (CoRR, 2024)

Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? - Buxin Su; Jiayao Zhang 001; Natalie Collina; Yuling Yan; Didong Li; Kyunghyun Cho; Jianqing Fan; Aaron Roth 001; Weijie J. Su (CoRR, 2024)

A Law of Next-Token Prediction in Large Language Models. - Hangfeng He 001; Weijie J. Su (CoRR, 2024)

A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem. - Weijie J. Su (CoRR, 2024)

The 2020 United States Decennial Census Is More Private Than You (Might) Think. - Buxin Su; Weijie J. Su; Chendi Wang (CoRR, 2024)

Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Models Alignment. - Mingzhi Wang; Chengdong Ma; Qizhi Chen; Linjian Meng; Yang Han; Jiancong Xiao; Zhaowei Zhang; Jing Huo; Weijie J. Su; Yaodong Yang 001 (CoRR, 2024)

Debiasing Watermarks for Large Language Models via Maximal Coupling. - Yangxinyu Xie; Xiang Li; Tanwi Mallick; Weijie J. Su; Ruixun Zhang (CoRR, 2024)

Robust Detection of Watermarks for Large Language Models Under Human Edits. - Xiang Li; Feng Ruan; Huiyuan Wang; Qi Long; Weijie J. Su (CoRR, 2024)

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy. - Jörg Drechsler; Ruobin Gong; Weijie Su 001; Linjun Zhang (J. Priv. Confidentiality, 2024)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic. - Matteo Sordello; Niccolò Dalmasso; Hangfeng He 001; Weijie J. Su (Trans. Mach. Learn. Res., 2024)

Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic. - Ruochen Jin; Bojian Hou; Jiancong Xiao; Weijie J. Su; Li Shen 001 (ICLR, 2025)

Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment. - Mingzhi Wang; Chengdong Ma; Qizhi Chen; Linjian Meng; Yang Han; Jiancong Xiao; Zhaowei Zhang; Jing Huo; Weijie J. Su; Yaodong Yang 001 (ICLR, 2025)

Towards Rationality in Language and Multimodal Agents: A Survey. - Bowen Jiang; Yangxinyu Xie; Xiaomeng Wang 002; Yuan Yuan; Zhuoqun Hao; Xinyi Bai; Weijie J. Su; Camillo Jose Taylor; Tanwi Mallick (NAACL (Long Papers), 2025)

An Overview of Large Language Models for Statisticians. - Wenlong Ji; Weizhe Yuan; Emily J. Getzen; Kyunghyun Cho; Michael I. Jordan; Song Mei; Jason Aaron Edward Weston; Weijie J. Su; Jing Xu; Linjun Zhang (CoRR, 2025)

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium. - Kaizhao Liu; Qi Long; Zhekun Shi; Weijie J. Su; Jiancong Xiao (CoRR, 2025)

A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation. - Yangxinyu Xie; Bowen Jiang; Tanwi Mallick; Joshua David Bergerson; John K. Hutchison; Duane R. Verner; Jordan Branham; M. Ross Alexander; Robert B. Ross; Yan Feng; Leslie-Anne Levy; Weijie Su 001; et al. (CoRR, 2025)

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach. - Jiancong Xiao; Bojian Hou; Zhanliang Wang; Ruochen Jin; Qi Long; Weijie J. Su; Li Shen 001 (CoRR, 2025)

Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching. - Zhekun Shi; Kaizhao Liu; Qi Long; Weijie J. Su; Jiancong Xiao (CoRR, 2025)

Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? - Xiang Li; Jiayi Xin; Qi Long; Weijie J. Su (CoRR, 2025)

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory. - Jiancong Xiao; Zhekun Shi; Kaizhao Liu; Qi Long; Weijie J. Su (CoRR, 2025)

Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts. - Xiang Li; Garrett Wen; Weiqing He; Jiayuan Wu; Qi Long; Weijie J. Su (CoRR, 2025)