The Geometry of Mixability
Cabrera.Pacheco, Armando J., and Robert C Williamson. 2023. “The Geometry of Mixability”. Transactions on Machine Learning Research.
Laura Iacovissi
PhD Student
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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.
Bob's Inaugural Lecture
Bob Williamson gave his inaugural lecture at the University of Tübingen recently. The photo shows the FMLS group at the reception. A video recording of the lecture can be found here. A pdf of the slides is here.
Raphael Sayer
PhD Student
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.
A Strictly Frequentist Theory of Imprecise Probability
From the Origin of Probability Theory to the Current Problems of Machine Learning
Machine learning is mainly standing on the pillars of probability theory and statistics. It was Kolmogorov's celebrated book ``Grundbegriffe der Wahrscheinlichkeitsrechnung'' (translated as ''Foundations of the Theory of Probability'') which in 1933 laid the foundation for modern probability theory...
Strictly Frequentist Imprecise Probability
Fröhlich, Christian, Rabanus Derr, and Robert C Williamson. 2024. “Strictly Frequentist Imprecise Probability”. International Journal of Approximate Reasoning 168 (109148).
Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates
Fröhlich, Christian, and Robert C. Williamson. 2023. “Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates”. ArXiv.