21 citations · 21 across the 7 of their papers we have counts for
7 papers
Confidence Calibration of Deep Learning Systems
Coby Penso
In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predicted probabilities reflect the l…
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…
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…
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…
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…
A Study on the Evaluation of Generative Models
Eyal Betzalel, Coby Penso, Aviv Navon +1
Implicit generative models, which do not return likelihood values, such as generative adversarial networks and diffusion models, have become prevalent in recent years. While it is…
Functional Ensemble Distillation
Coby Penso, Idan Achituve, Ethan Fetaya
Bayesian models have many desirable properties, most notable is their ability to generalize from limited data and to properly estimate the uncertainty in their predictions. However…