5 papers
Theoretical Foundations of Conformal Prediction
Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates
This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of ta…
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
Shai Feldman, Stephen Bates, Yaniv Romano
We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal predi…
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…
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…
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…