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.

A set function f on the subsets of a set E is called submodular if it satisfies a natural diminishing returns property: for any two subsets S \subseteq T \subseteq E and an element x outside T, we have f(T + x) - f(T) \leq f(S+x) - f(S).