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Zhaosong Lu

38 papers hereh-index 344.3k citations87 works total

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

author position
  • sole author7
  • first author16
  • middle author8
  • last author6

Across the 37 of 38 papers where every author was matched, so the position is known.

fields
  • math.OC28
  • cs.LG4
  • stat.ME3
  • stat.CO2
  • cs.CV1
same name
  • Zhaosong Lu — 8 papers, h 5
  • Zhaosong Lu — 5 papers, h 3
  • Zhaosong Lu — 1 paper, h 1

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
20092023
most citedA General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems

289 citations · 518 across the 37 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2014★ 2 cited

Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion

Zheng Wang, Ming-Jun Lai, Zhaosong Lu +3

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend orthogonal matching pursuit method from the vector case to the m…

cs.LG2013★ 289 cited

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems

Pinghua Gong, Changshui Zhang, Zhaosong Lu +2

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over…

cs.LG2012★ 14 cited

Fused Multiple Graphical Lasso

Sen Yang, Zhaosong Lu, Xiaotong Shen +2

In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar struc…

cs.LG2012★ 12 cited

Sparse Approximation via Penalty Decomposition Methods

Zhaosong Lu, Yong Zhang

In this paper we consider sparse approximation problems, that is, general l0​ minimization problems with the l0​-"norm" of a vector being a part of constraints or objective fun…

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