Journal article
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Stat (International Statistical Institute), Vol.12(1), e582
12/2023
DOI: 10.1002/sta4.582
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.
Details
- Title: Subtitle
- The ART of Transfer Learning: An Adaptive and Robust Pipeline
- Creators
- Boxiang Wang - University of IowaYunan Wu - The University of Texas at DallasChenglong Ye - University of Kentucky
- Resource Type
- Journal article
- Publication Details
- Stat (International Statistical Institute), Vol.12(1), e582
- DOI
- 10.1002/sta4.582
- ISSN
- 2049-1573
- eISSN
- 2049-1573
- Language
- English
- Electronic publication date
- 05/08/2023
- Date published
- 12/2023
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984413069202771
Metrics
9 Record Views