The problem of quickest detection of a change in the distribution of a
sequence of independent observations is considered. It is assumed that the
pre-change distribution is known (accurately estimated), while the only
information about the post-change distribution is through a (small) set of
labeled data. This post-change data is used in a data-driven minimax robust
framework, where an uncertainty set for the post-change distribution is
constructed using the Wasserstein distance from the empirical distribution of
the data. The robust change detection problem is studied in an asymptotic
setting where the mean time to false alarm goes to infinity, for which the
least favorable post-change distribution within the uncertainty set is the one
that minimizes the Kullback-Leibler divergence between the post- and the
pre-change distributions. It is shown that the density corresponding to the
least favorable distribution is an exponentially tilted version of the
pre-change density and can be calculated efficiently. A Cumulative Sum (CuSum)
test based on the least favorable distribution, which is referred to as the
distributionally robust (DR) CuSum test, is then shown to be asymptotically
robust. The results are extended to the case where the post-change uncertainty
set is a finite union of multiple Wasserstein uncertainty sets, corresponding
to multiple post-change scenarios, each with its own labeled data. The proposed
method is validated using synthetic and real data examples