Motivated by the increasing adoption of models which facilitate greater automation in risk management and decision-making, this talk presents a novel Importance Sampling (IS) scheme for estimating distribution tails for a rich class of objectives

In Reinforcement Learning, one often needs to evaluate a given policy using rewards observed by following another policy. This is called off-policy evaluation in Learning Theory parlance.

We construct a succinct non-interactive publicly-verifiable delegation scheme for any logspace uniform circuit under the sub-exponential Learning With Errors (LWE) assumption.

Symmetry reduction is a well studied subject in geometric mechanics, where symmetries are usually described as an invariance under an action of a Lie group.