Master Thesis: Conditional Normalising Flows for Interpretability Permalink
This work aims to fill the gap of two existing interpretability methods for global feature importance (Relative feature importance and Shapley additive global importance), by using deep conditional density estimator/sampler – Conditional Normalising Flow. After utilising noise regularisation, they do not require a rigorous hyperparameter search and could be used in the vanilla setting. We make an extensive empirical evaluation on different synthetic and real datasets with the help of a self-designed evaluation benchmark. Ground-truth feature importances are in general intractable, thus we inherited the concepts of strong and weak feature relevance, which have one-to-one relation to the causal structure of data generating mechanism. By utilising continuous datasets with a known causal graph, we can reason about the validity of estimated importances. Additionally, we provided a use case of RFI for detection of influence of sensitive attributes, when they are included in predictive modelling or completely ignored. This thesis extends the existing Python library for Relative Feature Importance, with Conditional Normalising Flow and Mixture Density Network, as well as with the synthetic benchmark.
Employing uncertanty estimation in pseudo-labelling for semi-supervised image classification methods (Fix-Match). As 
Semi-supervised text classification based on BERT backbone. The project adapts 
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