IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease Prediction.

Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in general in computer vision; yet, in the medical domain, it requires further examination. Most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the output of the model in a post-hoc fashion. In this paper, we propose an interpretable attention module (IAM) that explains the relevance of the input features to the classification task on a GNN Model. The model uses these interpretations to improve its performance. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. The main novelty lies in the IAM, which directly operates on input features. IAM learns the attention for each feature based on the unique interpretability-specific losses. We show the application of our model on two publicly available datasets, Tadpole and the UK Biobank (UKBB). For Tadpole we choose the task of disease classification, and for UKBB, age, and sex prediction. The proposed model achieves an increase in an average accuracy of 3.2% for Tadpole and 1.6% for UKBB sex and 2% for the UKBB age prediction task compared to the state-of-the-art. Further, we show exhaustive validation and clinical interpretation of our results.

Gamma and vega hedging using deep distributional reinforcement learning.

We show how reinforcement learning can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta-neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.