Provably consistent partial-label learning

Lei Feng*, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An*, Masashi Sugiyama

*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods - none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.
Original languageEnglish
Title of host publication34th Conference on Neural Information Processing Systems (NeurIPS 2020)
EditorsH. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, H. Lin
PublisherNeural Information Processing Systems Foundation
Pages10948-10960
Number of pages13
Volume14
ISBN (Print)9781713829546
DOIs
Publication statusPublished - 6 Dec 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: 6 Dec 202012 Dec 2020

Publication series

NameAdvances in Neural Information Processing Systems
Volume33
ISSN (Print)1049-5258
NameNeurIPS Proceedings

Conference

Conference34th Conference on Neural Information Processing Systems, NeurIPS 2020
CityVirtual, Online
Period6/12/2012/12/20

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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