Given sets of observations of training and test data, the authors consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. They achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature space (specifically, a reproducing kernel Hilbert space). This approach does not require distribution estimation. Instead, the sample weights are obtained by a simple quadratic programming procedure.