Can Machine Learning Generalize Beyond the Training Distribution?

There are some kinds of distribution shifts that are just impossible to handle, especially if the system is only given one opportunity or one chance to do it. The really common assumption in machine learning is that the data is kind of independently and identically distributed between training and testing. And so we introduce these new assumptions that allow us to actually improve generalization in very specific circumstances.