The art of transfer learning: An adaptive and robust pipeline

Boxiang Wang, Yunan Wu, Chenglong Ye

Research output: Contribution to journalArticlepeer-review

Abstract

Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the nonasymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART-integrated-aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real-data analysis for a mortality study.

Original languageEnglish
Article numbere582
JournalStat
Volume12
Issue number1
DOIs
StatePublished - Jan 1 2023

Bibliographical note

Publisher Copyright:
© 2023 John Wiley & Sons Ltd.

Keywords

  • auxiliary data
  • model aggregation
  • negative transfer
  • transfer learning
  • variable importance

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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