Further, we emphasize the significance of principled regularization of the network parameters and prediction.

However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for

Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations.

Often, observational data alone is not enough to uniquely identify a system's causal structure.

Marginal MAP is a widely applicable mixed inference task that identifies the most likely assignment for a subset of variables (called MAP variables).