518 citations · 1.3k across the 51 of their papers we have counts for
6 papers · 1 filter
The FDR-Linking Theorem
Weijie J. Su
This paper introduces the \texttt{FDR-linking} theorem, a novel technique for understanding \textit{non-asymptotic} FDR control of the Benjamini--Hochberg (BH) procedure under arbi…
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations
Bin Shi, Simon S. Du, Michael I. Jordan +1
Gradient-based optimization algorithms can be studied from the perspective of limiting ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not disti…
Differentially Private False Discovery Rate Control
Cynthia Dwork, Weijie J. Su, Li Zhang
Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false di…
Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Edgar Dobriban, Weijie J. Su, Yachong Yang +1
Drawing statistical inferences from large datasets in a model-robust way is an important problem in statistics and data science. In this paper, we propose methods that are robust t…
Assumption Lean Regression
Richard A. Berk, Andreas Buja, Lawrence Brown +4
It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim t…
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an…