3 papers
cs.LG2026
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume…
cs.AI2026
Unbiased Prevalence Estimation with Multicalibrated LLMs
Fridolin Linder, Thomas Leeper, Daniel Haimovich +3
Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to science, pub…
cs.LG2026
Dealing with Uncertainty in Contextual Anomaly Detection
Luca Bindini, Lorenzo Perini, Stefano Nistri +2
Contextual anomaly detection (CAD) aims to identify anomalies in a target (behavioral) variable conditioned on a set of contextual variables that influence the normalcy of the targ…