3 papers
cs.LG2025
Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi +3
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantific…
stat.ML2025
Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev +4
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary…
stat.ML2025
Rectifying Conformity Scores for Better Conditional Coverage
Vincent Plassier, Alexander Fishkov, Victor Dheur +4
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score…