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Thesis

Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques

Abstract:

Gaining insights into complex high-dimensional data is challenging and typically requires the use of dimensionality reduction methods. These methods let us identify low-dimensional structures embedded within the data that may reveal patterns of interest. In probabilistic models, such low-dimensional structures are captured via latent variables.

In biomedical applications, e.g. in computational biology, it is common to use *linear* dimensionality reduction approaches like ...

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Division:
MPLS
Department:
Statistics
Role:
Author

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Supervisor
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Supervisor
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Grant:
Programme:
EPSRC & MRC Centre for Doctoral Training (CDT) in Next Generational Statistical Science
Funding agency for:
Martens, K
More from this funder
Grant:
Programme:
EPSRC & MRC Centre for Doctoral Training (CDT) in Next Generational Statistical Science
Funding agency for:
Martens, K
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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