MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation
arXiv:2605.21783v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to prediction reliability. We develop a PAC-Bayesian framework yielding generalization bounds explicitly parameterized by the maximum mean discrepancy (MMD) between source and target distributions. Our principal contribution is interpreting MMD-balls around the source distribution as credal sets in Walley's imprecise probability theory, yielding natural epistemic uncertainty quantification. We establish: (i)
This research addresses a critical limitation of current AI models by providing guarantees for performance under distribution shifts, a common problem in real-world deployments.
Formal guarantees for AI reliability are essential for deploying autonomous systems in high-stakes environments, reducing risk and accelerating adoption.
AI models can now be developed with better-understood uncertainty quantification, moving from opaque statistical performance to formally bounded reliability under varying conditions.
- · AI safety researchers
- · Autonomous vehicle developers
- · Robotics industry
- · Regulators in AI-heavy sectors
- · Developers of ad-hoc, un-guaranteed AI systems
- · Companies relying on opaque AI black boxes
Increased trust and faster adoption of AI systems in safety-critical applications.
Development of new AI evaluation and certification standards based on formal reliability guarantees.
Reduced liability for AI system providers due to provable performance bounds, impacting insurance and legal frameworks.
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Read at arXiv cs.LG