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CrowdTeacher: Robust Co-teaching with Noisy Answers and Sample-Specific Perturbations for Tabular Data

  • 2021
  • OriginalPaper
  • Chapter
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Abstract

The chapter 'CrowdTeacher: Robust Co-teaching with Noisy Answers and Sample-Specific Perturbations for Tabular Data' addresses the challenge of training classifiers with noisy crowdsourced labels. It introduces CrowdTeacher, a method that integrates Co-teaching with sample-specific perturbations to improve the robustness of classifiers. The approach leverages the certainty of sample labels from label aggregation to inform the perturbation process, ensuring that the classifier learns more effectively from reliable data. The chapter also explores the use of data synthesizers to generate synthetic samples for perturbation and incorporates knowledge distillation to enhance performance on smaller tabular datasets. Experimental results on synthetic and real-world datasets demonstrate the superior predictive performance of CrowdTeacher compared to existing methods. The chapter is a valuable resource for researchers and practitioners seeking innovative solutions for training classifiers with noisy and uncertain data.

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Metadata
Title
CrowdTeacher: Robust Co-teaching with Noisy Answers and Sample-Specific Perturbations for Tabular Data
Authors
Mani Sotoodeh
Li Xiong
Joyce Ho
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-75765-6_15

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