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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.
On the Richness of Calibration
Höltgen, Benedikt, and Robert C Williamson. 2023. “On the Richness of Calibration”. In FAccT2023.
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
Mansour, Yishay, Richard Nock, and Robert C. Williamson. 2023. “Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice”. In ICML2023.
Systems of Precision: Coherent Probabilities on Pre-Dynkin Systems and Coherent Previsions on Linear Subspaces
Derr, Rabanus, and Robert C. Williamson. 2023. “Systems of Precision: Coherent Probabilities on Pre-Dynkin Systems and Coherent Previsions on Linear Subspaces”. Entropy 25 (9): 1283.
Geometry and Stability of Supervised Learning Problems
Mémoli, Facundo, Brantley Vose, and Robert C. Williamson. 2024. “Geometry and Stability of Supervised Learning Problems”. ArXiv.
Assessing AI Fairness in Finance
McCalman, Lachlan, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C Williamson, and Richard Zemel. 2022. “Assessing AI Fairness in Finance”. Computer 55 (1): 94-97.
Information Processing Equalities and the Information-Risk Bridge
Williamson, Robert C, and Zac Cranko. 2024. “Information Processing Equalities and the Information-Risk Bridge”. Journal of Machine Learning Research.
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
Derr, Rabanus, and Robert C Williamson. 2022. “Fairness and Randomness in Machine Learning: Statistical Independence and Relativization”. ArXiv Preprint arXiv:2207.13596.
The Set Structure of Precision: Coherent Probabilities on Pre-Dynkin-Systems
Derr, Rabanus, and Robert C Williamson. 2023. “The Set Structure of Precision: Coherent Probabilities on Pre-Dynkin-Systems”. ArXiv arXiv:2302.03522.