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20222026
most citedQuantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?

11 citations · 14 across the 9 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cs.LG2023★ 1 cited

Second-Order Uncertainty Quantification: Variance-Based Measures

Yusuf Sale, Paul Hofman, Lisa Wimmer +2

Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…

cs.LG2023

Conformal Prediction with Partially Labeled Data

Alireza Javanmardi, Yusuf Sale, Paul Hofman +1

While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or…