5 citations · 5 across the 2 of their papers we have counts for
4 papers
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
Raphael Sonabend, Andreas Bender, Sebastian Vollmer
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination. This is a non-trivial problem as discrimination measures are the most c…
Mitigating Statistical Bias within Differentially Private Synthetic Data
Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson +3
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data. However, mechanis…
Measuring Sample Quality with Diffusions
Jackson Gorham, Andrew B. Duncan, Sebastian J. Vollmer +1
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associat…