RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. Yoon, J., Jordon, J., & van der Schaar, M. In 35th International Conference on Machine Learning, ICML 2018, volume 13, pages 9069–9071, February, 2018. proceedings.mlr.press. _eprint: 1802.06403

Paper abstract bibtex

Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for \textbackslashldots

@inproceedings{yoon_radialgan_2018,
	title = {{RadialGAN}: {Leveraging} multiple datasets to improve target-specific predictive models using {Generative} {Adversarial} {Networks}},
	volume = {13},
	isbn = {978-1-5108-6796-3},
	url = {http://arxiv.org/abs/1802.06403},
	abstract = {Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for {\textbackslash}ldots},
	booktitle = {35th {International} {Conference} on {Machine} {Learning}, {ICML} 2018},
	publisher = {proceedings.mlr.press},
	author = {Yoon, Jinsung and Jordon, James and van der Schaar, Mihaela},
	month = feb,
	year = {2018},
	note = {\_eprint: 1802.06403},
	pages = {9069--9071},
}

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