Specifically, we lay the foundation within the Bayesian framework.

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

We compute the convergence rates of the RSVB approximate posterior and the corresponding optimal value.

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

Imitation learning uses data for training policies to solve complex tasks.