Distributionally Robust Active Learning for Gaussian Process Regression

Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively collects data labels to achieve an accurate prediction with fewer data labels, is an important problem. However, existing AL methods do not theoretically guarantee prediction accuracy for target distribution. Furthermore, as discussed in the distributionally robust learning literature, specifying the target distribution is often difficult. Thus, this paper proposes two AL methods that effectively reduce the worst-case expected error for GPR, which is the worst-case expectation in target distribution candidates. We show an upper bound of the worst-case expected squared error, which suggests that the error will be arbitrarily small by a finite number of data labels under mild conditions. Finally, we demonstrate the effectiveness of the proposed methods through synthetic and real-world datasets.

@article{takeno2025_2502.16870,
  title={ Distributionally Robust Active Learning for Gaussian Process Regression },
  author={ Shion Takeno and Yoshito Okura and Yu Inatsu and Aoyama Tatsuya and Tomonari Tanaka and Akahane Satoshi and Hiroyuki Hanada and Noriaki Hashimoto and Taro Murayama and Hanju Lee and Shinya Kojima and Ichiro Takeuchi },
  journal={arXiv preprint arXiv:2502.16870},
  year={ 2025 }
}