Wedesign a novel truncation operation to further reduce the computational complexity of theproposed algorithms.

A central question in computational neuroscience is how structure determines functioninneuralnetworks.

This application is often needed in safety-critical domains suchashealthcare, whereestimating andcommunicating uncertainty to decision-makers iscrucial. Weintroduce apractical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in highdimensional data, where standard applications of causal effect approaches fail.

In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data augmentation techniques specifically designed for the constraints of differentially private learning.