arXiv:2606. 26457v1 Announce Type: cross Abstract: This paper presents a probabilistic framework for online test-time adaptation problems.
By Daniel Corrales, David R\'ios Insua
arXiv:2606. 31420v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enables models trained on a source domain to adapt online to unlabeled test data under distribution shifts.
By Shaoyang Huang, Yashi Zhu, Yichen Yu, Lei Zhang, Zhang Yi, Tao He
arXiv:2607. 23735v1 Announce Type: new Abstract: In many real-world scenarios, encountering continual shifts in domain during inference is very common.
By Anurag Roy, Riddhiman Moulick, Vinay Kumar Verma, Saptarshi Ghosh, Abir Das
arXiv:2607. 18899v1 Announce Type: new Abstract: Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time.
By Giuseppe Soriano, Nicola Tonellotto, Alberto Gotta
arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.
By Mayank Sharma, Rohit Kumar Mourya, Pratik Mazumder
arXiv:2602. 06136v2 Announce Type: replace Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled samples.
By Sudarshan Sreeram, Young D. Kwon, Cecilia Mascolo