518 citations · 1.3k across the 51 of their papers we have counts for
14 papers · 1 filter
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review
Yuling Yan, Weijie J. Su, Jianqing Fan
In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we…
Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit
Zhiqi Bu, Jason Klusowski, Cynthia Rush +1
Sorted l1 regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. I…
A Power Analysis for Model-X Knockoffs with -Regularized Statistics
Asaf Weinstein, Weijie J. Su, Małgorzata Bogdan +2
Variable selection properties of procedures utilizing penalized-likelihood estimates is a central topic in the study of high dimensional linear regression problems. Existing litera…
The Complete Lasso Tradeoff Diagram
Hua Wang, Yachong Yang, Zhiqi Bu +1
A fundamental problem in the high-dimensional regression is to understand the tradeoff between type I and type II errors or, equivalently, false discovery rate (FDR) and power in v…
The Price of Competition: Effect Size Heterogeneity Matters in High Dimensions
Hua Wang, Yachong Yang, Weijie J. Su
In high-dimensional sparse regression, would increasing the signal-to-noise ratio while fixing the sparsity level always lead to better model selection? For high-dimensional sparse…
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…
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…
When Is the First Spurious Variable Selected by Sequential Regression Procedures?
Weijie J. Su
Applied statisticians use sequential regression procedures to produce a ranking of explanatory variables and, in settings of low correlations between variables and strong true effe…
Group SLOPE - adaptive selection of groups of predictors
Damian Brzyski, Weijie Su, Małgorzata Bogdan
Sorted L-One Penalized Estimation is a relatively new convex optimization procedure which allows for adaptive selection of regressors under sparse high dimensional designs. Here we…
Private False Discovery Rate Control
Cynthia Dwork, Weijie Su, Li Zhang
We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certai…
False Discoveries Occur Early on the Lasso Path
Weijie Su, Malgorzata Bogdan, Emmanuel Candes
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the importa…
SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax
Weijie Su, Emmanuel Candes
We consider high-dimensional sparse regression problems in which we observe , where is an design matrix and is an -dimensional vector of indepen…