Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
arXiv:2505.02743v2 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process. Mean-variance estimation networks can learn this type of uncertainty but require ad-hoc regularization strategies to avoid overfitting and are unable to predict epistemic uncertainty (model uncertainty). Conversely, Bayesian neural networks predict epistemic uncertainty but are notoriously difficult to train due to the approximate nature of Bayesian inference. We propose to coop
The continuous drive for more robust and reliable AI systems, especially in high-stakes applications, necessitates better uncertainty quantification techniques.
Improved methods for disentangling aleatoric and epistemic uncertainties will lead to more trustworthy and explainable AI models, crucial for widespread adoption and regulatory acceptance.
The proposed 'cooperative variance estimation' offers a pathway to combine the strengths of mean-variance networks and Bayesian neural networks, addressing longstanding limitations in uncertainty quantification.
- · AI researchers
- · Developers of safety-critical AI systems
- · Industries requiring high AI reliability (e.g., autonomous driving, healthcare)
- · Systems relying on ad-hoc uncertainty regularisation
- · Purely frequentist uncertainty methods
- · Black-box AI models without uncertainty estimates
More accurate and interpretable AI models become available for practical applications.
Increased trust in AI systems could accelerate adoption across sensitive domains, impacting regulatory frameworks.
Broader understanding and quantification of AI uncertainty might lead to new paradigms in risk management and compliance for AI-driven processes.
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