A probabilistic framework for online test-time adaptation

Daniel Corrales; David Ríos Insua

A probabilistic framework for online test-time adaptation

机器学习 2026-06-24 v1 机器学习

作者: Daniel Corrales , David Ríos Insua

摘要

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.

引用

@article{arxiv.2606.26457,
  title  = {A probabilistic framework for online test-time adaptation},
  author = {Daniel Corrales and David Ríos Insua},
  journal= {arXiv preprint arXiv:2606.26457},
  year   = {2026}
}