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Thesis

Generative modelling for supervised, unsupervised and private learning

Abstract:

In this thesis we develop several state-of-the-art generative modelling-based approaches for a variety of supervised, unsupervised and private learning problems. In the (almost) supervised domain, we tackle the problems of treatment effect estimation, imputation and feature selection. For treatment effect estimation we begin by developing a GAN-based approach that generates the ``missing'' counterfactuals, which enables learning a fully supervised model. In SCIGAN, we then go on to adapt t...

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Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Oxford college:
Wolfson College
Role:
Author

Contributors

Institution:
University of Cambridge
Role:
Supervisor
Role:
Supervisor
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Funding agency for:
Jordon, J
Grant:
EP/N509711/1
EP/R513295/1
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Funding agency for:
Jordon, J
Grant:
N00014-17-1-2215
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
Deposit date:
2022-03-14

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