General Uncertainty Estimation with Delta Variances

📰 ArXiv cs.AI

Learn to estimate uncertainty in neural networks using Delta Variances, a computationally efficient algorithm for epistemic uncertainty quantification

advanced Published 1 May 2026

Action Steps

Apply Delta Variances to a neural network to estimate epistemic uncertainty
Run simulations to compare the performance of Delta Variances with other uncertainty estimation methods
Configure a neural network to output uncertainty estimates using Delta Variances
Test the robustness of Delta Variances on different datasets and models
Compare the computational efficiency of Delta Variances with other uncertainty estimation algorithms

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this technique to improve the reliability of their models, especially when working with limited data

Key Insight

💡 Delta Variances is a computationally efficient algorithm for epistemic uncertainty quantification in neural networks

Key Takeaways

Learn to estimate uncertainty in neural networks using Delta Variances, a computationally efficient algorithm for epistemic uncertainty quantification

Full Article

Title: General Uncertainty Estimation with Delta Variances

Abstract:
arXiv:2502.14698v2 Announce Type: replace-cross Abstract: Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is however challenging to estimate efficiently for large neural networks. To this extent we investigate Delta Variances, a family of algorithms for epistemic uncertainty quantification, that is computationally efficient and convenient to implement. It can be applied to neural networks and more general f

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