31 citations · 43 across the 9 of their papers we have counts for
9 papers
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…
Multicalibration for Unbiased Model-Based Prevalence Estimation
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…
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…
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
Lorenzo Perini, Maja Rudolph, Sabrina Schmedding +1
Anomaly detection is the task of identifying examples that do not behave as expected. Because anomalies are rare and unexpected events, collecting real anomalous examples is often…
Deep Neural Network Benchmarks for Selective Classification
Andrea Pugnana, Lorenzo Perini, Jesse Davis +1
With the increasing deployment of machine learning models in many socially sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplis…
Unsupervised Anomaly Detection with Rejection
Lorenzo Perini, Jesse Davis
Anomaly detection aims at detecting unexpected behaviours in the data. Because anomaly detection is usually an unsupervised task, traditional anomaly detectors learn a decision bou…
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection
Lorenzo Perini, Daniele Giannuzzi, Jesse Davis
Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous la…
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
Lorenzo Perini, Paul Buerkner, Arto Klami
Anomaly detection methods identify examples that do not follow the expected behaviour, typically in an unsupervised fashion, by assigning real-valued anomaly scores to the examples…
Machine Learning with a Reject Option: A survey
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas +2
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can…