  With increasing volume of data being used across machine learning tasks, the
capability to target specific subsets of data becomes more important. To aid in
this capability, the recently proposed Submodular Mutual Information (SMI) has
been effectively applied across numerous tasks in literature to perform
targeted subset selection with the aid of a exemplar query set. However, all
such works are deficient in providing theoretical guarantees for SMI in terms
of its sensitivity to a subset's relevance and coverage of the targeted data.
For the first time, we provide such guarantees by deriving similarity-based
bounds on quantities related to relevance and coverage of the targeted data.
With these bounds, we show that the SMI functions, which have empirically shown
success in multiple applications, are theoretically sound in achieving good
query relevance and query coverage.
