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.
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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.
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.
Data Models With Two Manifestations of Imprecision
Frölich, Christian, and Robert C. Williamson. 2024. “Data Models With Two Manifestations of Imprecision”. ArXiv.
Which distribution were you sampled from? Towards a more tangible conception of data
Höltgen, Benedikt, and Robert C. Williamson. 2024. “Which Distribution Were You Sampled From? Towards a More Tangible Conception of Data”. ArXiv.
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
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.
Geometry and Stability of Supervised Learning Problems
Mémoli, Facundo, Brantley Vose, and Robert C. Williamson. 2024. “Geometry and Stability of Supervised Learning Problems”. ArXiv.
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.
Causal modelling without introducing counterfactuals or abstract distributions
Höltgen, Benedikt, and Robert C. Williamson. 2024. “Causal Modelling Without Introducing Counterfactuals or Abstract Distributions”. ArXiv.
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
Pacheco, Armando J. Cabrera, Rabanus Derr, and Robert C. Williamson. 2024. “An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm”. ArXiv.