3 papers
stat.ML2026
Parameter-Free and Group Conditional Online Conformal Prediction
Beepul Bharti, Ambar Pal, Jacopo Teneggi +1
Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data ma…
cs.LG2025
Direct Preference Optimization for Adaptive Concept-based Explanations
Jacopo Teneggi, Zhenzhen Wang, Paul H. Yi +2
Concept-based explanation methods aim at making machine learning models more transparent by finding the most important semantic features of an input (e.g., colors, patterns, shapes…
cs.CV2025
Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography
Jacopo Teneggi, J Webster Stayman, Jeremias Sulam
Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improve patient care. Beyond measuring…