We demonstrate the generality of this framework by recovering and extending previously known information-theoretic bounds.

The main idea is to use a generator to synthesize data for training the student.

This work was fully funded by NVIDIA.

Similarly, a user may want to interpolate different shapes.

This makes it crucial to align autonomous AI systems with their users' intentions. Precisely specifying which behaviours are or are not desirable is challenging, however. One approach to this specification problem is to learn an approximation of the true reward function (Ng et al., 2000;