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cs.LG2025
Regularized GLISp for sensor-guided human-in-the-loop optimization
Matteo Cercola, Michele Lomuscio, Dario Piga +1
Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While ef…
cs.LG2025
Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference
Matteo Cercola, Valeria Capretti, Simone Formentin
Learning from human preferences is a cornerstone of aligning machine learning models with subjective human judgments. Yet, collecting such preference data is often costly and time-…