From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps
Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than individual observations. We develop an approximation-theoretic framework for distribution-to-distribution learning and extend it to mappings between stochastic processes. Fo...