The variational fair autoencoder

4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings

Volumn , Issue , 2016, Pages

  Louizos, Christos ^a   Swersky, Kevin ^b   Li, Yujia ^b   Welling, Max ^a,c,d   Zemel, Richard ^b,c  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF TORONTO   (Canada)

^c CANADIAN INSTITUTE FOR ADVANCED RESEARCH   (Canada)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO ENCODERS; LATENT FACTOR; PENALTY TERM; SOURCES OF VARIATION;

EID: 85083951172     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. Analysis of representations for domain adaptation. Advances in neural information processing systems, 19:137, 2007.
2. Ben-David, Shai, Blitzer, John, Crammer, Koby, Kulesza, Alex, Pereira, Fernando, and Vaughan, Jennifer Wortman. A theory of learning from different domains. Machine learning, 79(1-2): 151–175, 2010.
3. Chen, Minmin, Xu, Zhixiang, Weinberger, Kilian, and Sha, Fei. Marginalized denoising autoencoders for domain adaptation. International Conference on Machine Learning (ICML), 2012.
4. Frank, A. and Asuncion, A. UCI machine learning repository, 2010. URL http://archive.ics.uci.edu/ml.
5. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. Domain-Adversarial Training of Neural Networks. ArXiv e-prints, May 2015.
6. Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte, Schölkopf, Bernhard, and Smola, Alex J. A kernel method for the two-sample-problem. In Advances in neural information processing systems, pp. 513–520, 2006.
7. Kifer, Daniel, Ben-David, Shai, and Gehrke, Johannes. Detecting change in data streams. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pp. 180–191. VLDB Endowment, 2004.
8. Kingma, Diederik P and Ba, Jimmy. Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR), 2015.
9. Kingma, Diederik P and Welling, Max. Auto-encoding variational bayes. International Conference on Learning Representations (ICLR), 2014.
10. Kingma, Diederik P, Mohamed, Shakir, Rezende, Danilo Jimenez, and Welling, Max. Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems, pp. 3581–3589, 2014.
11. Li, Yujia, Swersky, Kevin, and Zemel, Richard. Learning unbiased features. arXiv preprint arXiv:1412.5244, 2014.
12. Long, Mingsheng and Wang, Jianmin. Learning transferable features with deep adaptation networks. arXiv preprint arXiv:1502.02791, 2015.
13. Rahimi, Ali and Recht, Benjamin. Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. In Advances in neural information processing systems, pp. 1313–1320, 2009.
14. Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan. Stochastic backpropagation and approximate inference in deep generative models. International Conference on Machine Learning (ICML), 2014.
15. Tzeng, Eric, Hoffman, Judy, Zhang, Ning, Saenko, Kate, and Darrell, Trevor. Deep domain confusion: Maximizing for domain invariance. CoRR, abs/1412.3474, 2014. URL http://arxiv.org/abs/1412.3474.
16. van der Maaten, L. Barnes-Hut-SNE. ArXiv e-prints, January 2013.
17. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. Learning fair representations. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 325–333, 2013.
18. Zhao, Ji and Meng, Deyu. Fastmmd: Ensemble of circular discrepancy for efficient two-sample test. Neural computation, 2015.