Deep Learning

Poster presentation highlighting our project on scaling up causal recourse by designing a novel amortized framework based on causal generative models and neural network based causal intervention predictors.

We explore improving the practicality of providing causal recourse explanations through a novel neural network model-based automation framework.

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural …

Virtual invited presentation of our project on the usefulness of unlabeled data when modeling fair decision-making policies.

Virtual presentation of our project on the usefulness of unlabeled data when modeling fair decision-making policies.

We explore the helpfulness of unlabeled data for fair, optimal and stable decision-making in societal settings.

Presented Master thesis work as part of my defense.

Project that was conducted as part of my Master’s thesis to explore the application of generative models to compute counterfactuals for fairness.

This project explores the case for potential bias in generative models such as variational autoencoders. The project also briefly looks at ways to mitigate such bias.

Exploring potential use cases of variational autoencoders in the context of robustness of ML systems.