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cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
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…