@article{Minami_Liu_Wu_Fukumizu_Yoshida_2021, title={A General Class of Transfer Learning Regression without Implementation Cost}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17087}, DOI={10.1609/aaai.v35i10.17087}, abstractNote={We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretrained neural networks. Moreover, the proposed method can benefit from its simple implementation without any additional cost; the regression model can be fully trained using off-the-shelf libraries for supervised learning in which the original output variable is simply transformed to a new output variable. We demonstrate its simplicity, generality, and applicability using various real data applications.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Minami, Shunya and Liu, Song and Wu, Stephen and Fukumizu, Kenji and Yoshida, Ryo}, year={2021}, month={May}, pages={8992-8999} }