mlsanityData to 7 Oct 2026

The ART of Transfer Learning: An Adaptive and Robust Pipeline

arXiv 2305.00520 ↗First posted 30 Apr 2023stat.ML cs.LGcited by 1

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Abstract (arXiv)

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 non-asymptotic 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 30 Apr 2023.

Builds on

15 of 54 references.

References · 15

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  3. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  4. 2018Characterizing and Avoiding Negative Transfercited by 226
  5. 2020On the Theory of Transfer Learning: The Importance of Task Diversitycited by 186
  6. 2020Provable Meta-Learning of Linear Representationscited by 161
  7. 2018An introduction to domain adaptation and transfer learningcited by 112
  8. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 89
  9. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 69
  10. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 64
  11. 2018Transfer learning for time series classificationcited by 60
  12. 2020A No-Free-Lunch Theorem for MultiTask Learningcited by 28
3 more
  1. 2018Predicting with Proxies: Transfer Learning in High Dimensioncited by 24
  2. 2017Overcoming data scarcity with transfer learningcited by 14
  3. 2022Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)cited by 6

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