• Corpus ID: 237417334

A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms

@article{Wu2021ABA,
  title={A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms},
  author={Xuetong Wu and Jonathan H. Manton and Uwe Aickelin and Jingge Zhu},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.01377}
}
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could be useful for solving a related task, if not executed properly, transfer learning algorithms can impair the learning performance instead of improving it — commonly known as negative transfer. In this paper, we study transfer learning from a Bayesian perspective, where a parametric statistical model is used… 

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