Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

ArXi:2605.10654v1 Announce Type: cross We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distribution induced by the function itself. This self-induced weighting arises naturally in problems such as potential energy surface (PES) modeling in computational chemistry, yet poses unique challenges as the target distribution is unknown and its partition function is intractable.