An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
Pacheco, Armando J. Cabrera, Rabanus Derr, and Robert C. Williamson. 2024. “An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm”. ArXiv.
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Geometry and Stability of Supervised Learning Problems
Mémoli, Facundo, Brantley Vose, and Robert C. Williamson. 2025. “Geometry and Stability of Supervised Learning Problems”. Journal of Machine Learning Research 26 (221): 1-99.
The Geometry and Calculus of Losses
Williamson, Robert C, and Zac Cranko. 2023. “The Geometry and Calculus of Losses”. Journal of Machine Learning Research 24 (342): 1-72.
On the Richness of Calibration
Höltgen, Benedikt, and Robert C Williamson. 2023. “On the Richness of Calibration”. In FAccT2023 .
The Geometry of Mixability
Cabrera.Pacheco, Armando J., and Robert C Williamson. 2023. “The Geometry of Mixability”. Transactions on Machine Learning Research.
The AI of ethics
Williamson, Robert C. 2021. “The AI of Ethics”. In Machines We Trust: Perspectives on Dependable AI, 139-60. Boston: MIT Press.
Scoring Rules and Calibration for Imprecise Probabilities
Fröhlich, Christian, and Robert C. Williamson. 2024. “Scoring Rules and Calibration for Imprecise Probabilities”. ArXiv.
Three Types of Calibration with Properties and theirSemantic and Formal Relationships
Derr, Rabanus, Jessie Finocchiaro, and Robert C. Williamson. 2025. “Three Types of Calibration With Properties and TheirSemantic and Formal Relationships”. ArXiv 2504.18395.