Paper on Calibration at FaCCT
Our paper on The Richness of Calibration has appeared in the proceedings of FaCCT 2023
Bob Williamson
Professor of Foundations of Machine Learning Systems
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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.
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Information Processing Equalities
machine learning processes information, but what do we mean by 'information"? One view comes from Information Theory, which was originally developed motivated by problems of communication. But learning problems are different, aren't they? This project explores the notion of information that makes...