One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators

ArXi:2605.06873v1 Announce Type: cross Probabilistic conditioning is concerned with the identification of a distribution of a random variable $X$ given a random variable $Y$. It is a cornerstone of scientific and engineering applications where modeling uncertainty is key. This problem has traditionally been addressed in machine learning by directly learning the conditional distribution of a fixed joint distribution. This paper