Linjun Zhang

Technical Reports

(* indicates alphabetical ordering authorship; ** indicates equal contribution)

• * T. Tony Cai, Yichen Wang and Linjun Zhang. (2023)
  Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning.
  submitted
• Xinming Tu, James Zou, Weijie Su and Linjun Zhang. (2023)
  What Should Data Science Education Do with Large Language Models?.
  submitted
• Mert Yuksekgonul, Linjun Zhang, James Zou, and Carlos Guestrin. (2023)
  Beyond Confidence: Reliable Models Should Also Consider Atypicality.
  submitted
• Peng Wang, Min-Ge Xie and Linjun Zhang. (2022)
  Finite-and Large-Sample Inference for Model and Coefficients in High-dimensional Linear Regression with Repro Samples .
  submitted
• Zhe Zhang and Linjun Zhang. (2021)
  High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees .
  submitted.
• * Maya Burhanpurkar, Zhun Deng, Cynthia Dwork and Linjun Zhang. (2021)
  Scaffolding Sets.
  submitted.
• Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou and Linjun Zhang. (2021)
  The Power of Contrast for Feature Learning: A Theoretical Analysis.
  submitted.
• * T. Tony Cai, Yichen Wang and Linjun Zhang. (2020)
  The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds.
  submitted.
• Xianli Zeng, Yingcun Xia, and Linjun Zhang. (2019)
  Double Cross Validation for The Number of Factors in Orthogonal Factor Models .
  submitted
• Linjun Zhang, Rong Ma, T. Tony Cai, and Hongzhe Li. (2020)
  Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression. .
  submitted

Publications

(* indicates alphabetical ordering authorship; ** indicates equal contribution)

Algorithmic Fairness

• * Zhun Deng, Cynthia Dwork and Linjun Zhang. (2023)
  HappyMap: A Generalized Multicalibration Method .
  ITCS 2023.
• Shirley Wu, Mert Yuksekgonul, Linjun Zhang and James Zou. (2023)
  Discover and Cure: Concept-aware Mitigation of Spurious Correlation .
  ICML 2023.
• Puheng Li, James Zou, and Linjun Zhang. (2023)
  FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee .
  ICLR 2023
• Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, and James Zou. (2023)
  FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data .
  ICLR 2023.
• Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang and David C. Parkes. (2023)
  Reinforcement Learning with Stepwise Fairness Constraints .
  AISTATS 2023.
• Haotian Ye, $James Zou, and $Linjun Zhang. (2023)
  Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise .
  AISTATS 2023
• Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou and Chelsea Finn. (2022)
  Improving Out-of-Distribution Robustness via Selective Augmentation .
  ICML 2022.



Private Data Analysis

• * T. Tony Cai, Yichen Wang and Linjun Zhang. (2023)
  Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning.
  
• * Jinshuo Dong, Weijie Su, and Linjun Zhang. (2021)
  A Central Limit Theorem for Differentially Private Query Answering.
  NeurIPS 2021, and selected as spotlight (top 3% of submissions)
• * T. Tony Cai, Yichen Wang and Linjun Zhang. (2021)
  The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy.
  Annals of Statistics .
• Zhe Zhang and Linjun Zhang. (2020)
  Privacy-Preserving Algorithms: the Gain and the Loss .
  CHANCE 33 (4), 22-28 .



Deep Learning (with focus on representation learning)

• Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou and Linjun Zhang. (2023)
  Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data .
  AISTATS 2023.
• Huaxiu Yao, Linjun Zhang and Chelsea Finn. (2022)
  Meta-Learning with Fewer Tasks through Task Interpolation .
  ICLR 2022, selected as the oral presentation (top 1.5% of submissions).
• Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou, and Chelsea Finn. (2022)
  C-Mixup: Improving Generalization in Regression .
  NeurIPS 2022.
• Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou and Chelsea Finn. (2022)
  Improving Out-of-Distribution Robustness via Selective Augmentation .
  ICML 2022.
• ** Linjun Zhang, ** Zhun Deng, Kenji Kawaguchi and James Zou. (2022)
  When and How Mixup Improves Calibration .
  ICML 2022.
• Kenji Kawaguchi, Linjun Zhang and Zhun Deng. (2022)
  Understanding Dynamics of Nonlinear Representation Learning and Its Application .
  Neural Computation.
• ** Zhun Deng, ** Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, and James Zou. (2021)
  Adversarial Training Helps Transfer Learning via Better Representations .
  NeurIPS 2021.
• Huaxiu Yao, Longkai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang and Zhenhui Li. (2021)
  Improving Generalization in Meta-learning via Task Augmentation .
  ICML 2021.
• **Linjun Zhang, **Zhun Deng, **Kenji Kawaguchi, Amirata Ghorbani, and James Zou. (2020)
  How Does Mixup Help With Robustness and Generalization? [Slides]
  ICLR 2021, and selected as spotlight (top 5% of submissions)
• ** Zhun Deng, ** Linjun Zhang, Amirata Ghorbani, and James Zou. (2020)
  Improving Adversarial Robustness via Unlabeled Out-of-Domain Data .
  AISTATS 2021, and selected as the oral presentation (top 3% of submissions)
• * Zhun Deng, Cynthia Dwork, Jialiang Wang, and Linjun Zhang. (2020)
  Interpreting Robust Optimization via Adversarial Influence Functions .
  ICML 2020



High-dimensional Statistics

• Sai Li, Linjun Zhang, T. Tony Cai, and Hongzhe Li. (2023)
  Estimation and Inference in High-Dimensional Generalized Linear Models with Knowledge Transfer.
  Journal of the American Statistical Association.
• Ruijia Wu, Linjun Zhang, and T. Tony Cai. (2021)
  Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference. .
  Journal of the American Statistical Association
• * T. Tony Cai, and Linjun Zhang. (2020)
  A Convex Optimization Approach to High-dimensional Sparse Quadratic Discriminant Analysis.
  Annals of Statistics
• * T. Tony Cai, and Linjun Zhang. (2019)
  High-dimensional Linear Discriminant Analysis: Optimality, Adaptivity, and Missing Data.
  Journal of Royal Statistical Society, B
• * T. Tony Cai, Jing Ma and Linjun Zhang. (2018)
  CHIME: Clustering of High-Dimensional Gaussian Mixtures with EM Algorithm and Its Optimality.
  Annals of Statistics
• * T. Tony Cai, Linjun Zhang and Harrison H. Zhou. (2017)
  Adaptive Functional Linear Regression.
  Statistica Sinica
• * T. Tony Cai, and Linjun Zhang. (2017)
  High-Dimensional Gaussian Copula Regression: Adaptive Estimation and Statistical Inference.
  Statistica Sinica, 2018, Vol. 28, 963-993.
• * T. Tony Cai, and Linjun Zhang. (2016)
  Discussion: Important feature PCA for high dimensional clustering.
  Annals of Statistics, 2016, Vol. 44, No. 6, 2372-2381.



Network Analysis

• Linjun Zhang, Michael Small, and Kevin Judd. (2015)
  Exactly scale-free scale-free networks.
  Physica A, 433: 182-197.
• Michael Small, Lvlin Hou, and Linjun Zhang. (2014)
  Random complex networks.
  The IEEE International Symposium on Circuits and Systems (ISCAS) 2014 invited paper.
• Michael Small, Kevin Judd, L. Zhang. (2014)
  How is that complex network complex?
  National Science Review , 1(3): 357-367.