There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models. Consequently, there is a growing interest in detecting these shifts before associated costs have time to accumulate. However, desiderata of crucial importance to the practicable deployment of sequential shift detectors are typically overlooked by existing works, precluding their widespread adoption. We identify three such desiderata, highlight existing works relevant to their satisfaction, and recommend impactful directions for future research.

翻译：随着部署的机器学习模型受分布漂移有害影响的认识日益加深，检测这些漂移以在相关代价累积前及时响应的需求日益增长。然而，现有工作普遍忽视了对于序列漂移检测器实际部署至关重要的关键准则，这阻碍了其广泛采用。我们识别出三项此类准则，梳理了与之相关的现有研究成果，并为未来研究指明了具有影响力的方向。