Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Neal Jean, Sang Michael Xie, Stefano Ermon
Abstract
Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.
BibTeX
@inproceedings{NEURIPS2018_9d28de8f,
author = {Jean, Neal and Xie, Sang Michael and Ermon, Stefano},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/9d28de8ff9bb6a3fa41fddfdc28f3bc1-Paper.pdf},
volume = {31},
year = {2018}
}