Transductive model selection under prior probability shift

Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the assumption according to which the training data and the unlabelled data are independently and identically distributed (IID), does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. By tailoring model selection to the actual test distribution, our approach contributes to the trustworthiness of AI systems, as it enables more reliable and robust classifier deployment under changed conditions. We provide experimental results that show the benefits brought about by our method.