The decision that a patient and a clinician jointly make when a donor organ is offered for transplantation is critical and carries serious consequences.

The softmax is the standard transformation used in machine learning to map real-valued vectors to categorical distributions.

An image of a room may be conjured given only the description of the underlying objects and their associated relations.

The variability of what "similar" means in this context, which transformations are allowed, and whether data points themselves or their feature representations

For this setting, we prove that entropy minimization on unlabeled target data will avoid using the spurious feature if initialized with a decently accurate source classifier, even though the objective is non-convex and contains multiple bad local minima using the spurious features.