Ng, YC;
Colombo, N;
Silva, R;
(2018)
Bayesian Semi-supervised Learning with Graph Gaussian Processes.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Neural Information Processing Systems 31.
Neural Information Processing Systems Foundation, Inc.: Montreal, Canada.
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Abstract
We propose a data-efficient Gaussian process-based Bayesian approach to the semisupervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.
Type: | Proceedings paper |
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Title: | Bayesian Semi-supervised Learning with Graph Gaussian Processes |
Event: | Neural Information Processing Systems 2018 |
Location: | Montreal, CANADA |
Dates: | 02 December 2018 - 08 December 2018 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://papers.nips.cc/paper/7440-bayesian-semi-su... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10072366 |
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