Data Models With Two Manifestations of Imprecision
Frölich, Christian, and Robert C. Williamson. 2024. “Data Models With Two Manifestations of Imprecision”. ArXiv.
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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.
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.
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.
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret
Derr, Rabanus, and Robert C. Williamson. 2024. “Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret.”
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.
Geometry and Stability of Supervised Learning Problems
Mémoli, Facundo, Brantley Vose, and Robert C. Williamson. 2025. “Geometry and Stability of Supervised Learning Problems”. Journal of Machine Learning Research 26 (221): 1-99.
Scoring Rules and Calibration for Imprecise Probabilities
Fröhlich, Christian, and Robert C. Williamson. 2024. “Scoring Rules and Calibration for Imprecise Probabilities”. ArXiv.