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stat.ML2025
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning
James McInerney, Nathan Kallus
Uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks. Existing methods quantify uncertainty at the last layer or other a…
stat.ML2024
Adjusting Regression Models for Conditional Uncertainty Calibration
Ruijiang Gao, Mingzhang Yin, James McInerney +1
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be i…