Generalization bounds for regularized pairwise learning

Yunwen Lei, Shao Bo Lin, Ke Tang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)

Abstract

Pairwise learning refers to learning tasks with the associated loss functions depending on pairs of examples. Recently, pairwise learning has received increasing attention since it covers many machine learning schemes, e.g., metric learning, ranking and AUC maximization, in a unified framework. In this paper, we establish a unified generalization error bound for regularized pairwise learning without either Bernstein conditions or capacity assumptions. We apply this general result to typical learning tasks including distance metric learning and ranking, for each of which our discussion is able to improve the state-of-the-art results.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2376-2382
Number of pages7
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - Jul 2018
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018
http://ijcai-18.org/
https://www.ijcai.org/proceedings/2018/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July
ISSN (Print)1045-0823

Conference

Conference27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Country/TerritorySweden
CityStockholm
Period13/07/1819/07/18
Internet address

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Kernel Methods
  • Learning Preferences or Rankings
  • Learning Theory
  • Machine Learning

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