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Thesis

Scalable inference and private co-training for Gaussian processes

Abstract:

Two principal problems are pursued in this thesis: that of scaling inference for Gaussian process regression to very large numbers of data points, and that of differentially private co-training between multiple Gaussian processes with distinct private views of the data.

The first chapter acts as an introduction to Bayesian nonparametric regression and standard techniques for performing scalable inference and differentially private communication with Gaussian Processes.

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Division:
MPLS
Department:
Statistics
Role:
Author

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Role:
Supervisor
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Name:
Engineering and Physical Sciences Research Council
Funding agency for:
Thomas, O
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
UUID:
uuid:f7282b97-431b-466d-b7a5-1b55e05dc250
Deposit date:
2018-02-19

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