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Nicolò Felicioni

2 accepted papers

2026

Measuring Uncertainty Calibration

ICLR 2026poster

We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that i…

Cited by 0SourcecodeScholar
2022

Off-Policy Evaluation with Deficient Support Using Side Information

NeurIPS 2022accept

The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of new policies from the data collected by another one. OPE is crucial when evaluating a new policy online is too expensive or risky. Many of the state-of-the-art OPE estimators are based on the Inverse Propensity Scoring…

Cited by 13SourcePDFScholar