Out-of-distribution generalization

Topic

Out-of-distribution (OOD) generalization is a subfield of machine learning that focuses on the ability of a model to perform well on test data whose underlying statistical distribution differs from that of the training data. Traditional machine learning paradigms assume that training and testing data are independent and identically distributed (i.i.d.), but OOD generalization addresses real-world scenarios where this assumption fails due to domain shifts, environmental changes, or selection biases. Research in this area involves developing robust algorithms, such as invariant learning and domain adaptation, to ensure models remain reliable and safe when deployed in unseen environments.