Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 2013 |
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Host publication | Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013 |
Pages | 282-290 |
Number of pages | 9 |
<mark>Original language</mark> | English |
Event | 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States Duration: 11/07/2013 → 15/07/2013 |
Conference | 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 |
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Country/Territory | United States |
City | Bellevue, WA |
Period | 11/07/13 → 15/07/13 |
Conference | 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 |
---|---|
Country/Territory | United States |
City | Bellevue, WA |
Period | 11/07/13 → 15/07/13 |
We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.