Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
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labelprop_and_meanteacher ssdkl code for UCI Oct 24, 2018
ssdkl ssdkl code for UCI Oct 24, 2018
vae ssdkl code for UCI Oct 24, 2018
.gitignore ssdkl code for UCI Oct 24, 2018
README.md ssdkl code for UCI Oct 24, 2018
setup.py ssdkl code for UCI Oct 24, 2018

README.md

Semi-supervised Deep Kernel Learning

This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Install via pip install -e . in this directory in a NEW virtualenv.

  • Experiments for SSDKL, DKL, VAT, Coreg are in the directory ssdkl.
  • Experiments for Label Propagation and Mean Teacher are in labelprop_and_meanteacher.
  • Experiments for VAE are in the directory vae.

For more detailed instructions, please see the README files in each directory.

Tested with Python 2.7.12.

If you find this code useful in your research, please cite

@article{jeanxieermon_ssdkl_2018,
  title={Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance},
  author={Jean, Neal and Xie, Sang Michael and Ermon, Stefano},
  journal={Neural Information Processing Systems (NIPS)},
  year={2018},
}