arXiv:2605.25882v1 Announce Type: new Abstract: Recent advances in uncertainty quantification increasingly emphasise the distinction between aleatory and epistemic uncertainty in machine learning, motivating the need for more unified frameworks. However, despite much progress in producing reliable predictions, existing methods often lack rigorous guarantees when generalising beyond the training domain. We propose a conformalised imprecise inference framework for robust extrapolation, which is model-agnostic and augments predictive models with imprecision and distance awareness. The proposed ap
The increasing complexity and safety concerns surrounding machine learning models, particularly in critical applications, necessitate robust uncertainty quantification methods capable of handling limited and out-of-distribution data.
This development offers a pathway to more reliable and trustworthy AI systems by providing rigorous guarantees for predictions, even when operating beyond their initial training domains.
Machine learning models can now be augmented with a framework that explicitly addresses both aleatory and epistemic uncertainty, enabling more robust extrapolation and reducing the risk of unexpected failures.
- · AI developers
- · High-stakes ML applications (e.g., healthcare, autonomous systems)
- · Regulatory bodies
- · Industries reliant on data-driven decision making
- · AI systems lacking robust uncertainty mechanisms
- · Predictive models prone to brittle extrapolation
Improved reliability and safety in deployed AI/ML systems.
Increased adoption of AI in domains previously hesitant due to uncertainty concerns, fostering innovation and economic growth.
Potential for new regulatory frameworks and industry standards centered around certified uncertainty quantification for AI models, influencing market design.
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