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researcher

Weijie Su

University of Pennsylvania

53 papers hereh-index 284.7k citations77 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author3
  • first author5
  • middle author19
  • last author23

Across the 50 of 53 papers where every author was matched, so the position is known.

fields
  • math.ST14
  • stat.ML14
  • cs.LG12
  • stat.ME6
  • cs.CR3
  • math.OC2
affiliations
  • University of Pennsylvania
same name
  • Weijie Su — 13 papers, h 11
  • Weijie Su — 13 papers, h 10
  • Weijie Su — 7 papers, h 4
  • Weijie Su — 5 papers, h 2
  • Weijie Su — 3 papers
  • Weijie Su — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedA Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

518 citations · 1.3k across the 51 of their papers we have counts for

collaborators
Showing 2015 · math.STShow all

4 papers · 2 filters

math.ST2015

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…

math.ST2015★ 20 cited

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…

math.ST2015★ 17 cited

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…

math.ST2015★ 11 cited

SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax

Weijie Su, Emmanuel Candes

We consider high-dimensional sparse regression problems in which we observe y=Xβ+z, where X is an n×p design matrix and z is an n-dimensional vector of indepen…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.