Standard generalization guarantees in machine learning rely heavily on the independent and identically distributed (IID) assumption. If this assumption is relaxed—meaning that the underlying data distributions shift between the training phase and the testing phase—then no claims can be made about the model's ability to generalize to new data unless additional assumptions are established. Specifically, if the training data is sampled from a distribution $p_S(\mathbf{x},y)$ and the test data from a different distribution $p_T(\mathbf{x},y)$, learning a robust classifier is impossible absent any assumptions on how the distributions relate. Fortunately, under restricted assumptions about the shift, principled algorithms can detect and adapt to changes.