Research

My research interests are in high-dimensional statistics, statistical machine learning, deep learning theory, privacy-preserving data analysis, and large-scale multiple testing.

Papers

  • On Learning Rates and Schrödinger Operators
    Bin Shi, Weijie Su, and Michael Jordan. [pdf][arXiv]

  • Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
    Qinqing Zheng, Jinshuo Dong, Qi Long, and Weijie Su. [pdf][arXiv]

  • Deep Learning with Gaussian Differential Privacy
    Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie Su. [pdf][arXiv]

  • Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
    Matteo Sordello and Weijie Su. [pdf][arXiv]

  • The Local Elasticity of Neural Networks
    Hangfeng He and Weijie Su. [pdf][arXiv]

  • Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
    Zhiqi Bu, Jason Klusowski, Cynthia Rush, and Weijie Su. [pdf][arXiv]

  • Gaussian Differential Privacy
    Jinshuo Dong, Aaron Roth, and Weijie Su. [pdf][arXiv]

  • Acceleration via Symplectic Discretization of High-Resolution Differential Equations
    Bin Shi, Simon Du, Weijie Su, and Michael Jordan. [pdf][arXiv]

  • The FDR-Linking Theorem
    Weijie Su [pdf][arXiv]

  • Understanding the Acceleration Phenomenon via High-Resolution Differential Equations
    Bin Shi, Simon Du, Michael Jordan, and Weijie Su. [pdf][arXiv]

  • Differentially Private False Discovery Rate Control
    Cynthia Dwork, Weijie Su, and Li Zhang. [pdf][arXiv]

  • Robust Inference Under Heteroskedasticity via the Hadamard Estimator
    Edgar Dobriban and Weijie Su. [pdf][arXiv]

  • Uncertainty Quantification for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
    Weijie Su and Yuancheng Zhu. [pdf][arXiv][webpage]

  • Adaptive Filtering Procedures for Replicability Analysis of High-throughput Experiments
    Jingshu Wang, Weijie Su, Chiara Sabatti, and Art Owen. [pdf][arXiv]

  • Assumption Lean Regression
    Richard Berk, Andreas Buja, Lawrence Brown, Edward George, Arun Kuchibhotla, Weijie Su, and Linda Zhao. The American Statistician, to appear. [pdf][arXiv]

  • Multiple Testing When Many p-Values Are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions
    Qingyuan Zhao, Dylan Small, and Weijie Su. Journal of the American Statistical Association, 114(527), 1291–1304, 2019. [pdf][arXiv]

  • Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
    Tengyuan Liang and Weijie Su. Journal of the Royal Statistical Society: Series B (Methodological), , 81(2), 431–456, 2019. [pdf][arXiv]

  • Group SLOPE – Adaptive Selection of Groups of Predictors
    Damian Brzyski, Alexej Gossmann, Weijie Su, and Małgorzata Bogdan. Journal of the American Statistical Association, 114(525), 419–433, 2019. [pdf][arXiv][code]

  • When Is the First Spurious Variable Selected by Sequential Regression Procedures?
    Weijie Su. Biometrika, 105 (3), 517–527, 2018. [pdf][arXiv][journal]

  • False Discoveries Occur Early on the Lasso Path
    Weijie Su, Małgorzata Bogdan, and Emmanuel Candès. Annals of Statistics, 45(5), 2133–2150, 2017. [pdf][supp][arXiv][journal][code]

  • A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
    Weijie Su, Stephen Boyd, and Emmanuel Candès. Journal of Machine Learning Research, 17(153), 1–43, 2016. [pdf][arXiv][journal][BibTex]

  • SLOPE Is Adaptive to Unknown Sparsity and Asymptotically Minimax
    Weijie Su and Emmanuel Candès. Annals of Statistics, 44(3), 1038–1068, 2016. [pdf][supp][arXiv][journal]

  • Familywise Error Rate Control via Knockoffs
    Lucas Janson and Weijie Su. Electronic Journal of Statistics, 10(1), 960–975, 2016. [pdf][arXiv][journal][code]

  • SLOPE – Adaptive Variable Selection via Convex Optimization
    Małgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su, and Emmanuel Candès. Annals of Applied Statistics, 9(3), 1103–1140, 2015. [pdf][arXiv][journal][code]

  • A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
    Weijie Su, Stephen Boyd, and Emmanuel Candès. In Neural Information Processing Systems, 2510–2518, 2014. [pdf][conference]