Andrew L. Beam

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

2020 · arXiv (Cornell University)

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model's uncertainty is evaluated using point-prediction metrics such as negative …