Semi-supervised Triply Robust Inductive Transfer Learning-Reference-Cited by-同舟云学术

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Semi-supervised Triply Robust Inductive Transfer Learning

Published:2024-08-22 Issue: Volume: Page:1-22
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Cai Tianxi¹²^ORCID,Li Mengyan³,Liu Molei⁴

Affiliation:

1. Department of Biostatistics, Harvard T.H. Chan School of Public Health

2. Department of Biomedical Informatics, Harvard Medical School

3. Department of Mathematical Sciences, Bentley University

4. Department of Biostatistics, Columbia University Mailman School of Public Health

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2393463

Reference43 articles.

1. Abuduweili A. Li X. Shi H. Xu C.-Z. and Dou D. (2021) Adaptive consistency regularization for semi-supervised transfer learning in ‘Proceedings of the IEEE/CVF conference on computer vision and pattern recognition’ pp. 6923–6932.

2. Ashley E. A. (2016) ‘Towards precision medicine’ Nature Reviews Genetics 17(9) 507–522.

3. Athey S. Tibshirani J. and Wager S. (2019) ‘Generalized random forests’ The Annals of Statistics 47(2) 1148–1178.

4. Bastani H. (2021) ‘Predicting with proxies: Transfer learning in high dimension’ Management Science 67(5) 2964–2984.

5. Billings L. K. and Florez J. C. (2010) ‘The genetics of type 2 diabetes: what have we learned from gwas?’ Annals of the New York Academy of Sciences 1212(1) 59–77.

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