Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization-Reference-Cited by-同舟云学术

Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization

Published:2022-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Cheng Mingyuan¹,Liao Xinru²,Liu Quan²,Ma Bin²,Xu Jian¹,Zheng Bo¹

Affiliation:

1. Alibaba Group, Beijing, China

2. Alibaba Group, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3532011

Reference28 articles.

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3. Artem Betlei Eustache Diemert and Massih-Reza Amini. 2020. Treatment Targeting by AUUC Maximization with Generalization Guarantees. arXiv:2012.09897 [cs.LG] Artem Betlei Eustache Diemert and Massih-Reza Amini. 2020. Treatment Targeting by AUUC Maximization with Generalization Guarantees. arXiv:2012.09897 [cs.LG]

4. T. Chen Xuechen Li Roger B. Grosse and D. Duvenaud. 2018. Isolating Sources of Disentanglement in Variational Autoencoders. In NeurIPS. T. Chen Xuechen Li Roger B. Grosse and D. Duvenaud. 2018. Isolating Sources of Disentanglement in Variational Autoencoders. In NeurIPS.

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