Funder
National Natural Science Foundation of China
Reference51 articles.
1. Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation;Alexandari,2020
2. Importance weight estimation and generalization in domain adaptation under label shift;Azizzadenesheli;IEEE Transactions on Pattern Analysis and Machine Intelligence,2022
3. Regularized learning for domain adaptation under label shifts;Azizzadenesheli,2019
4. A theory of learning from different domains;Ben-David;Machine Learning,2010
5. Cao, Z., Ma, L., Long, M., & Wang, J. (2018). Partial adversarial domain adaptation. In Proceedings of the European conference on computer vision (pp. 135–150).