8 citations · 11 across the 20 of their papers we have counts for
25 papers
A Ranking Approach for Measuring Calibration
Anirban Chatterjee, Rina Foygel Barber
When providing forecasted probabilities with a predictive model, the ideal model offers perfect calibration: the true probability of the outcome (i.e., the probability that )…
Algorithmic stability via ensembling
Rina Foygel Barber, Richard J. Samworth
Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may vary depending on the setting.…
An analysis of binary isotonic regression: degrees of freedom and implications for calibration
Raphael Rossellini, Rina Foygel Barber, Zhimei Ren +1
Isotonic regression is a canonical tool for estimating monotone functions and calibrating probabilistic predictors. We provide a fully sharp finite-sample characterization of its w…
Local permutation tests for conditional independence: an adaptive binning perspective
David Chen, Rohan Hore, Rina Foygel Barber
In this work, we study the problem of testing conditional independence between random variables and given a confounder . The local permutation test (LPT) offers a princi…
Approximating full conformal prediction: distribution free guarantees via the tournament correction
Aabesh Bhattacharyya, Boxuan Zhang, Rina Foygel Barber
Conformal prediction is a framework for providing prediction intervals with distribution-free validity, guaranteeing predictive coverage for data drawn from any distribution. Its t…
Conformal Prediction with Macro-Coverage Guarantees
Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber
Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others. In the classification setting, class-conditional covera…