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cs.LG2024
Provable Uncertainty Decomposition via Higher-Order Calibration
Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan +2
We give a principled method for decomposing the predictive uncertainty of a model into aleatoric and epistemic components with explicit semantics relating them to the real-world da…
cs.LG2024
Taking a Moment for Distributional Robustness
Jabari Hastings, Christopher Jung, Charlotte Peale +1
A rich line of recent work has studied distributionally robust learning approaches that seek to learn a hypothesis that performs well, in the worst-case, on many different distribu…