A distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on merely detecting if a shift has occurred and assumes any detected shift can be understood and handled appropriately by a human operator. We hope to aid in these manual mitigation tasks by explaining the distribution shift using interpretable transportation maps from the original distribution to the shifted one. We derive our interpretable mappings from a relaxation of the optimal transport problem, where the candidate mappings are restricted to a set of interpretable mappings. We then use quintessential examples of distribution shift in simulated and real-world cases to showcase how our explanatory mappings provide a better balance between detail and interpretability than the de facto standard mean shift explanation by both visual inspection and our PercentExplained metric.
翻译:分配变化可产生根本性后果,例如显示操作环境的变化或显著降低下游模型的准确性。因此,理解分配变化对于检查和希望减轻这种转变的影响至关重要。大多数先前的工作都仅仅侧重于检测是否发生了转变,假设任何检测到的转变都可以由人类操作者理解和妥善处理。我们希望通过使用从原始分布到转移的可解释运输图解释分配变化,来帮助完成这些人工缓解任务。我们从最佳运输问题的缓和中得出可解释的绘图,其中候选绘图仅限于一套可解释的绘图。我们然后使用模拟和实际案例的典型分布变化实例来展示我们的解释性绘图如何在细节和可解释性之间提供更好的平衡,而不是通过视觉检查和我们 Percent Explated 的参数来解释事实上的标准转移解释。