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- W4375951932 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." @default.
- W4375951932 created "2023-05-10" @default.
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- W4375951932 date "2023-05-23" @default.
- W4375951932 modified "2023-09-29" @default.
- W4375951932 title "The art of transfer learning: An adaptive and robust pipeline" @default.
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- W4375951932 doi "https://doi.org/10.1002/sta4.582" @default.
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