3 citations · 3 across the 6 of their papers we have counts for
6 papers
CalArena: A Large-Scale Post-Hoc Calibration Benchmark
Eugène Berta, David Holzmüller, Francis Bach +1
Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibration provides a simple and wi…
V-ECE: Estimating General Expected Calibration Errors
Eugène Berta, Sacha Braun, David Holzmüller +2
In probabilistic classification, calibration error (CE) measures the average divergence of predicted probabilities from , the true class distribution for…
Structured Matrix Scaling for Multi-Class Calibration
Eugène Berta, David Holzmüller, Michael I. Jordan +1
Post-hoc recalibration methods are widely used to ensure that classifiers provide faithful probability estimates. We argue that parametric recalibration functions based on logistic…
Multivariate Standardized Residuals for Conformal Prediction
Sacha Braun, Eugène Berta, Michael I. Jordan +1
While split conformal prediction guarantees marginal coverage, approaching the stronger property of conditional coverage is essential for reliable uncertainty quantification. Naive…
Rethinking Early Stopping: Refine, Then Calibrate
Eugène Berta, David Holzmüller, Michael I. Jordan +1
Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these predi…
Classifier Calibration with ROC-Regularized Isotonic Regression
Eugene Berta, Francis Bach, Michael Jordan
Calibration of machine learning classifiers is necessary to obtain reliable and interpretable predictions, bridging the gap between model confidence and actual probabilities. One p…