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researcher

Yuanyuan Liu

28 papers hereh-index 262.1k citations88 works total

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

author position
  • first author1
  • middle author22
  • last author3

Across the 26 of 28 papers where every author was matched, so the position is known.

fields
  • cs.LG19
  • cs.CV5
  • math.NA2
  • cs.IT1
  • stat.ML1
same name
  • Yuanyuan Liu — 16 papers, h 10
  • Yuanyuan Liu — 6 papers, h 13
  • Yuanyuan Liu — 4 papers, h 3
  • Yuanyuan Liu — 4 papers, h 1
  • Yuanyuan Liu — 3 papers, h 10
  • Yuanyuan Liu — 3 papers, h 3

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 citedVideo Super Resolution Based on Deep Learning: A Comprehensive Survey

40 citations · 223 across the 28 of their papers we have counts for

collaborators
Showing 2017Show all

3 papers · 1 filter

cs.LG2017★ 25 cited

Accelerated Variance Reduced Stochastic ADMM

Yuanyuan Liu, Fanhua Shang, James Cheng

Recently, many variance reduced stochastic alternating direction method of multipliers (ADMM) methods (e.g.\ SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made exciting progress such as…

cs.LG2017★ 20 cited

Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning

Fanhua Shang, Yuanyuan Liu, James Cheng +1

Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems). However, the…

cs.LG2017★ 2 cited

Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent

Fanhua Shang, Yuanyuan Liu, James Cheng +2

In this paper, we propose a novel sufficient decrease technique for variance reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In order to make sufficient dec…

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