5 papers
Law of Large Numbers: Accuracy as Statistical Measure for AI Compliance and Competition
Rabanus Derr, Alina Wernick, Robert C. Williamson
The machine learning community progresses (in part) by improving the "accuracy" of its systems. The EU AI Act explicitly refers to "accuracy" as part of its compliance measures for…
Comparing Corrupted Constrained Learning Problems
Laura Iacovissi, Rabanus Derr, Robert C. Williamson
A key result in statistics is the data processing inequality, originally proved by Blackwell (1951) and later refined by DeGroot (1962) in terms of statistical uncertainty. The lat…
Three Types of Calibration with Properties and their Semantic and Formal Relationships
Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson
Fueled by discussions around "trustworthiness" and algorithmic fairness, calibration of predictive systems has regained scholars attention. The vanilla definition and understanding…
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
Armando J. Cabrera Pacheco, Rabanus Derr, Robert C. Williamson
Supervised learning has gone beyond the expected risk minimization framework. Central to most of these developments is the introduction of more general aggregation functions for lo…
Evaluation Metrics as Averaged Outcomes of Fair Gambles
Rabanus Derr, Robert C. Williamson
In the current practices of machine learning, the evaluation of forecasts has become a cornerstone of scientific progress. A multitude of evaluation metrics have been suggested and…