A probabilistic framework for online test-time adaptation
Daniel Corrales, David Ríos Insua
stat.ML cs.LG
Abstract
This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to unlabeled data at test time under the assumption that training and test distributions potentially differ, that is, there might have been a distributional shift. The framework is based on a state-space modelling architecture from which parameter learning, parameter time evolution, prior tuning, and prediction can be characterized.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.