collaborators

5 papers

cs.LG2025

Privacy-Preserving Conformal Prediction Under Local Differential Privacy

Coby Penso, Bar Mahpud, Jacob Goldberger +1

Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relies on a calibration set with clea…

cs.LG2025

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

Coby Penso, Jacob Goldberger, Ethan Fetaya

Conformal Prediction (CP) controls the prediction uncertainty of classification systems by producing a small prediction set, ensuring a predetermined probability that the true clas…

cs.LG2024

Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated Accuracy

Coby Penso, Jacob Goldberger

This study addresses the problem of calibrating network confidence while adapting a model that was originally trained on a source domain to a target domain using unlabeled samples…

cs.LG2024

A conformalized learning of a prediction set with applications to medical imaging classification

Roy Hirsch, Jacob Goldberger

Medical imaging classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, which prevents their deployment in medical clin…

cs.LG2024

A Conformal Prediction Score that is Robust to Label Noise

Coby Penso, Jacob Goldberger

Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study…