Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
arXiv:2603.16481v2 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. In this domain, kernel methods such as Gaussian process regression are established techniques, thanks to their inherent uncertainty quantification mechanism. Still, existing bounds either pose strong assumptions on the underlying noise distribution, are conservative, do not directly apply in the multi-output case, or are difficult to integrate into downstream tasks.
The paper addresses a long-standing challenge in reliable AI predictions, particularly relevant as AI systems move into safety-critical applications requiring robust uncertainty quantification.
Improved uncertainty bounds for kernel regression can enhance the reliability and trustworthiness of AI models, crucial for their broader adoption in autonomous systems and sensitive decision-making.
This research provides a method for generating more precise and less conservative uncertainty bounds in multivariate kernel regression, potentially leading to safer and more effective AI applications.
- · AI developers
- · Robotics industry
- · Safety-critical AI applications
- · Research in machine learning
- · Current methods relying on conservative uncertainty bounds
More accurate and reliable AI predictions are enabled for complex, multi-output systems.
This foundational improvement could accelerate the development and deployment of autonomous agents and control systems in real-world environments.
Increased trust in AI systems could unlock new economic sectors and lead to greater societal integration of AI with enhanced safety guarantees.
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Read at arXiv cs.LG