139 citations · 315 across the 36 of their papers we have counts for
36 papers
Corruptions of Supervised Learning Problems: Typology and Mitigations
Laura Iacovissi, Nan Lu, Robert C. Williamson
Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature predominantly focuses on specific settings and learning scenarios, lack…
Insights From Insurance for Fair Machine Learning
Christian Fröhlich, Robert C. Williamson
We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and…
The Geometry of Mixability
Armando J. Cabrera Pacheco, Robert C. Williamson
Mixable loss functions are of fundamental importance in the context of prediction with expert advice in the online setting since they characterize fast learning rates. By re-interp…
On the Richness of Calibration
Benedikt Höltgen, Robert C Williamson
Probabilistic predictions can be evaluated through comparisons with observed label frequencies, that is, through the lens of calibration. Recent scholarship on algorithmic fairness…
Systems of Precision: Coherent Probabilities on Pre-Dynkin-Systems and Coherent Previsions on Linear Subspaces
Rabanus Derr, Robert C. Williamson
In literature on imprecise probability little attention is paid to the fact that imprecise probabilities are precise on a set of events. We call these sets systems of precision. We…
Strictly Frequentist Imprecise Probability
Christian Fröhlich, Rabanus Derr, Robert C. Williamson
Strict frequentism defines probability as the limiting relative frequency in an infinite sequence. What if the limit does not exist? We present a broader theory, which is applicabl…