Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Fröhlich, Christian, and Robert C Williamson. 2023. “Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity”. Transactions on Machine Learning Research.
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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 .
Corruptions of Supervised Learning Problems: Typology and Mitigations
Iacovissi, Laura, Nan Lu, and Robert C. Williamson. 2024. “Corruptions of Supervised Learning Problems: Typology and Mitigations”. ArXiv.
Style of Reasoning of Machine Learning
The notion of a "style of reasoning" was developed by Ian Hacking, who explored in particular the statistical style of reasoning in XXX. In this project we build upon that work to analyse the style of reasoning of machine learning. We argue that (ironically) although ML makes great use of data, it...
The Style of Reasoning of Machine Learning
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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).
Limits to Predicting Online Speech Using Large Language Models
Remeli, Mina, Moritz Hardt, and Robert C. Williamson. 2024. “Limits to Predicting Online Speech Using Large Language Models”. ArXiv.