Deep learning for grouped data

This dissertation explores the problems inherent in applying deep learning algorithms to groups of data. My claim is that groups should be represented as random variables whose values should be inferred from data. This approach has the potential to unlock solutions in many important domains of machine learning, including disentangling the generative factors of data, performing missing data imputation, or training robust predictors. However, grouped data also comes with challenges, especially when the data is high-dimensional and non-linear. Addressing these limitations is the focus of the technical contributions of my doctorate.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Iliescu, Dan Andrei

Advisor dc:contributor.advisor

• Wischik, Damon

• deep learning
• variational autoencoders
• transformers
• machine learning
• bayesian inference
• causal inference
• self supervised learning
• latent space disentanglement
• missing data imputation
• unsupervised domain adaptation

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/95bcb94c-258e-4c1b-9305-5912d85590c0/download
• http://purl.org/NET/rdflicense/allrightsreserved

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.119538

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/386225