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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.

Williamson, Robert C, and Zac Cranko. 2024. “Information Processing Equalities and the Information-Risk Bridge”. Journal of Machine Learning Research.

Derr, Rabanus, and Robert C Williamson. 2023. “The Set Structure of Precision: Coherent Probabilities on Pre-Dynkin-Systems”. ArXiv arXiv:2302.03522.

Frölich, Christian, and Robert C. Williamson. 2024. “Data Models With Two Manifestations of Imprecision”. ArXiv.

Höltgen, Benedikt, and Robert C. Williamson. 2024. “Which Distribution Were You Sampled From? Towards a More Tangible Conception of Data”. ArXiv.

Derr, Rabanus, and Robert C Williamson. 2024. “Fairness and Randomness in Machine Learning: Statistical Independence and Relativization”. The New England Journal of Statistics in Data Science.

Mansour, Yishay, Richard Nock, and Robert C Williamson. 2022. “What Killed the Convex Booster?”. ArXiv Preprint ArXiv:2205.09628.

Fröhlich, Christian, and Robert C Williamson. 2023. “Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity”. Transactions on Machine Learning Research.

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.

Iacovissi, Laura, Nan Lu, and Robert C. Williamson. 2024. “Corruptions of Supervised Learning Problems: Typology and Mitigations”. ArXiv.