4 papers
Distributionally Robust Coreset Selection under Covariate Shift
Tomonari Tanaka, Hiroyuki Hanada, Hanting Yang +9
Coreset selection, which involves selecting a small subset from an existing training dataset, is an approach to reducing training data, and various approaches have been proposed fo…
Distributionally Robust Active Learning for Gaussian Process Regression
Shion Takeno, Yoshito Okura, Yu Inatsu +9
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 co…
Distributionally Robust Safe Sample Elimination under Covariate Shift
Hiroyuki Hanada, Tatsuya Aoyama, Satoshi Akahane +9
We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models ar…
Distributionally Robust Safe Screening
Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama +8
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method…