518 citations · 1.3k across the 51 of their papers we have counts for
7 papers · 1 filter
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…
Communication-Efficient False Discovery Rate Control via Knockoff Aggregation
Weijie Su, Junyang Qian, Linxi Liu
The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scien…
Familywise Error Rate Control via Knockoffs
Lucas Janson, Weijie Su
We present a novel method for controlling the -familywise error rate (-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Can…
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…
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Weijie Su, Stephen Boyd, Emmanuel J. Candes
We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov's…