This paper concerns designing  distributed algorithms that are singularly optimal, i.e., algorithms that are simultaneously time and message optimal, for the  fundamental leader election problem in networks.

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.