Partial identification of kernel based two sample tests with mismeasured
  data
Ron Nafshi Maggie Makar
Nonparametric two-sample tests such as the Maximum Mean Discrepancy (MMD) are
often used to detect differences between two distributions in machine learning
applications. However, the majority of existing literature assumes that
error-free samples from the two distributions of interest are available.We
relax this assumption and study the estimation of the MMD under
$\epsilon$-contamination, where a possibly non-random $\epsilon$ proportion of
one distribution is erroneously grouped with the other. We show that under
$\epsilon$-contamination, the typical estimate of the MMD is unreliable.
Instead, we study partial identification of the MMD, and characterize sharp
upper and lower bounds that contain the true, unknown MMD. We propose a method
to estimate these bounds, and show that it gives estimates that converge to the
sharpest possible bounds on the MMD as sample size increases, with a
convergence rate that is faster than alternative approaches. Using three
datasets, we empirically validate that our approach is superior to the
alternatives: it gives tight bounds with a low false coverage rate.