139 citations · 315 across the 36 of their papers we have counts for
6 papers · 1 filter
The Geometry and Calculus of Losses
Robert C. Williamson, Zac Cranko
Statistical decision problems lie at the heart of statistical machine learning. The simplest problems are binary and multiclass classification and class probability estimation. Cen…
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Christian Fröhlich, Robert C. Williamson
Expected risk minimization (ERM) is at the core of many machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its…
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
Rabanus Derr, Robert C. Williamson
Fair Machine Learning endeavors to prevent unfairness arising in the context of machine learning applications embedded in society. Despite the variety of definitions of fairness an…
Information Processing Equalities and the Information-Risk Bridge
Robert C. Williamson, Zac Cranko
We introduce two new classes of measures of information for statistical experiments which generalise and subsume -divergences, integral probability metrics, -dista…
Risk Measures and Upper Probabilities: Coherence and Stratification
Christian Fröhlich, Robert C. Williamson
Machine learning typically presupposes classical probability theory which implies that aggregation is built upon expectation. There are now multiple reasons to motivate looking at…
What killed the Convex Booster ?
Yishay Mansour, Richard Nock, Robert C. Williamson
A landmark negative result of Long and Servedio established a worst-case spectacular failure of a supervised learning trio (loss, algorithm, model) otherwise praised for its high p…