Domain Adaptation for Learning from Label Proportions Using Domain-Adversarial Neural Network

Author:

Li XintianORCID,Culotta Aron

Funder

national science foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference28 articles.

1. Ardehaly EM, Culotta A. Inferring latent attributes of twitter users with label regularization. In: Proceedings of the 2015 conference of the north american chapter of the association for computational linguistics: Human language technologies, 2015. p. 185–195.

2. Ardehaly EM, Culotta A. Domain adaptation for learning from label proportions using self-training. In: IJCAI, 2016. p. 3670–3676.

3. Ardehaly EM, Culotta A. Co-training for demographic classification using deep learning from label proportions. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE 2017. p. 1017–1024.

4. Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M. Unsupervised domain adaptation by domain invariant projection. In: Proceedings of the IEEE International Conference on Computer Vision, 2013. p. 769–776.

5. Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW. A theory of learning from different domains. Mach Learn. 2010;79(1):151–75.

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