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Shusen Wang

30 papers hereh-index 214.6k citations45 works total

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

author position
  • sole author4
  • first author10
  • middle author15
  • last author1

Across the 30 of 30 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • stat.ML10
  • cs.DC3
  • cs.IR1
  • cs.MS1
  • math.NA1
same name
  • Shusen Wang — 3 papers, h 3
  • Shusen Wang — 2 papers, h 2
  • Shusen Wang — 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
20122023
most citedOn the Convergence of FedAvg on Non-IID Data

979 citations · 1.5k across the 30 of their papers we have counts for

collaborators
Showing 2019 · stat.MLShow all

4 papers · 2 filters

stat.ML2019★ 28 cited

Communication-Efficient Local Decentralized SGD Methods

Xiang Li, Wenhao Yang, Shusen Wang +1

Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it…

stat.ML2019★ 4 cited

Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping

Shusen Wang

Random feature mapping (RFM) is a popular method for speeding up kernel methods at the cost of losing a little accuracy. We study kernel ridge regression with random feature mappin…

stat.ML2019★ 979 cited

On the Convergence of FedAvg on Non-IID Data

Xiang Li, Kaixuan Huang, Wenhao Yang +2

Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading algorithm in this setting, Federated Averaging (\tex…

stat.ML2019

Do Subsampled Newton Methods Work for High-Dimensional Data?

Xiang Li, Shusen Wang, Zhihua Zhang

Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.…

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