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stat.ML2026
Deep Ensembles for Epistemic Uncertainty: A Frequentist Perspective
Anchit Jain, Stephen Bates
Decomposing prediction uncertainty into aleatoric (irreducible) and epistemic (reducible) components is critical for the reliable deployment of machine learning systems. While the…
stat.ML2025
Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
David R. Burt, Renato Berlinghieri, Stephen Bates +1
Estimating associations between spatial covariates and responses - rather than merely predicting responses - is central to environmental science, epidemiology, and economics. For i…
stat.ML2025
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
Haichen Hu, Rui Ai, Stephen Bates +1
Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis…