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researcher

Q. Li

12 papers hereh-index 153.8k citations25 works total

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

author position
  • first author5
  • middle author6

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.CR4
same name
  • Q. Li — 440 papers
  • Q. Li — 154 papers, h 4
  • Q. Li — 142 papers, h 3
  • Q. Li — 113 papers, h 68
  • Q. Li — 110 papers, h 9
  • Q. Li — 94 papers, h 16

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
20192026
most citedA Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

1.4k citations · 1.6k across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 28 cited

Practical Federated Gradient Boosting Decision Trees

Qinbin Li, Zeyi Wen, Bingsheng He

Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several rec…

cs.LG2019★ 11 cited

Adaptive Kernel Value Caching for SVM Training

Qinbin Li, Zeyi Wen, Bingsheng He

Support Vector Machines (SVMs) can solve structured multi-output learning problems such as multi-label classification, multiclass classification and vector regression. SVM training…

cs.LG2019★ 8 cited

Privacy-Preserving Gradient Boosting Decision Trees

Qinbin Li, Zhaomin Wu, Zeyi Wen +1

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT whil…

cs.LG2019★ 1.4k cited

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Qinbin Li, Zeyi Wen, Zhaomin Wu +5

Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As…

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