Online Bipartitie Matching was first introduced by Karp, Vazirani and Vazirani (STOC’90). In their seminal paper they had inroduced the RANKING algorithm which admits a tight competitive ratio of 1-1/e.

In this talk, we will consider the problem of binary hypothesis testing with finite memory. Consider a sequence of IID random variables with expectation p under hypothesis H_0 and q under hypothesis H_1.

Multi-armed bandit (MAB) is a popular framework for decision-making in an uncertain environment. In its classical setup, the algorithm has access to a fixed and finite set of unknown, independent probability distributions or arms.