arXiv:2605.12208v2 Announce Type: replace-cross Abstract: Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of interest is often a model's predictions rather than its parameters. In this work, we propose to bypass the parameter posterior and focus directly on approximating the posterior predictive distribution. We achieve this by drawing inspiration from self-training within self-supervised and semi-supervised learning. Essentially, we quantify a Bayesian model's predictive uncertainty by refitting on
The increasing complexity of AI models necessitates more robust uncertainty quantification methods, and self-supervised approaches are gaining traction across various AI subfields.
Accurate uncertainty quantification is critical for deploying reliable AI systems, especially in high-stakes applications, enhancing trust and practical utility.
This method potentially offers a more direct and efficient way to assess model prediction reliability, bypassing computationally intensive parameter posterior computations.
- · AI developers
- · High-reliability AI applications
- · Users of AI systems
- · Traditional Bayesian inference methods
- · Computationally expensive uncertainty quantification techniques
Improved reliability and explainability of AI model predictions.
Faster development and deployment cycles for AI systems requiring strong uncertainty bounds.
Broader adoption of AI in sensitive domains where trust in predictions is paramount, potentially accelerating 'AI agents' capabilities.
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Read at arXiv cs.LG