Secure message transmission and Byzantine agreement have been studied extensively in incomplete networks. However, information theoretically secure multiparty computation (MPC) in incomplete networks is less well understood.

Distributed learning protocols are designed to train on distributed data without gathering it all on a single centralized machine, thus contributing to the efficiency of the system and enhancing its privacy.

The K-armed bandit problem is a sequential decision making problem wherein one has to sequentially sample from a given set of K probability distributions (belonging to a known family) informally called 'arms of the bandit'.

One of the paramount mathematical mysteries of our times is to be able to explain the phenomenon of deep-learning i.e training neural nets.