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Gaussian Process Conditional Density Estimation

Dutordoir, V; Salimbeni, H; Deisenroth, MP; Hensman, J; (2018) Gaussian Process Conditional Density Estimation. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018). Neural Information Processing Systems (NIPS): Montreal, QC, Canada. Green open access

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Abstract

Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challenging task as there is a fundamental trade-off between model complexity, representational capacity and overfitting. In this work, we propose to extend the model's input with latent variables and use Gaussian processes (GP) to map this augmented input onto samples from the conditional distribution. Our Bayesian approach allows for the modeling of small datasets, but we also provide the machinery for it to be applied to big data using stochastic variational inference. Our approach can be used to model densities even in sparse data regions, and allows for sharing learned structure between conditions. We illustrate the effectiveness and wide-reaching applicability of our model on a variety of real-world problems, such as spatio-temporal density estimation of taxi drop-offs, non-Gaussian noise modeling, and few-shot learning on omniglot images.

Type: Proceedings paper
Title: Gaussian Process Conditional Density Estimation
Event: 32nd Conference on Neural Information Processing Systems (NIPS 2018), 3-8 December 2018, Montreal, QC, Canada
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/7506-gaussian-process...
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 > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10083564
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