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With the rise of machine learning and deep learning based applications inpractice, monitoring, i.e. verifying that these operate within specification,has become an important practical problem. An important aspect of thismonitoring is to check whether the inputs (or intermediates) have strayed fromthe distribution they were validated for, which can void the performanceassurances obtained during testing.  There are two common approaches for this. The, perhaps, more classical one isoutlier detection or novelty detection, where, for a single input we askwhether it is an outlier, i.e. exceedingly unlikely to have originated from areference distribution. The second, perhaps more recent approach, is toconsider a larger number of inputs and compare its distribution to a referencedistribution (e.g. sampled during testing). This is done under the label driftdetection.  In this work, we bridge the gap between outlier detection and drift detectionthrough comparing a given number of inputs to an automatically chosen part ofthe reference distribution.

Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection