Searching to exploit memorization effect in learning with noisy labels

Quanming Yao*, Hansi Yang, Bo Han, Gang Niu, James Tin Yau Kwok

*Corresponding author for this work

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

53 Citations (Scopus)


Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memorization effect is a hard problem. In this paper, motivated by the success of automated machine learning (AutoML), we model this issue as a function approximation problem. Specifically, we design a domain-specific search space based on general patterns of the memorization effect and propose a novel Newton algorithm to solve the bi-level optimization problem efficiently. We further provide theoretical analysis of the algorithm, which ensures a good approximation to critical points. Experiments are performed on benchmark data sets. Results demonstrate that the proposed method is much better than the state-of-the-art noisy-label-learning approaches, and also much more efficient than existing AutoML algorithms.

Original languageEnglish
Title of host publicationProceedings of the 37th International Conference on Machine Learning
EditorsHal Daumé III, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Electronic)9781713821120
Publication statusPublished - Jul 2020
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498


Conference37th International Conference on Machine Learning, ICML 2020
Internet address

Scopus Subject Areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software


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