# Understanding the Under-Coverage Bias in Uncertainty Estimation

@inproceedings{Bai2021UnderstandingTU, title={Understanding the Under-Coverage Bias in Uncertainty Estimation}, author={Yu Bai and Song Mei and Huan Wang and Caiming Xiong}, booktitle={NeurIPS}, year={2021} }

Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input. It is frequently observed that quantile regression—a vanilla algorithm for learning quantiles with asymptotic guarantees—tends to under-cover than the desired coverage level in reality. While various fixes have been proposed, a more fundamental understanding of why this under-coverage bias happens in the first place remains…

## 3 Citations

Efficient and Differentiable Conformal Prediction with General Function Classes

- Computer ScienceArXiv
- 2022

This meta-algorithm generalizes existing conformal prediction algorithms, and it achieves approximate valid population coverage and near-optimal eﬃciency within class, whenever the function class in the conformalization step is low-capacity in a certain sense.

Theoretical characterization of uncertainty in high-dimensional linear classification

- Computer ScienceArXiv
- 2022

This manuscript characterise uncertainty for learning from limited number of samples of high-dimensional Gaussian input data and labels generated by the probit model, and provides a closed-form formula for the joint statistics between the logistic classifier, the uncertainty of the statistically optimal Bayesian classifier and the ground-truth probit uncertainty.

Quantifying Epistemic Uncertainty in Deep Learning

- Computer ScienceArXiv
- 2021

A theoretical framework to dissect the uncertainty, especially the epistemic component, in deep learning into procedural variability and data variability is provided, which is the first such attempt in the literature to the authors' best knowledge.

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