Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization
We consider a stochastic optimization problem involving two random variables: a context variable $X$ and a dependent variable $Y$. The objective is to minimize the expected value of a nonlinear loss functional applied to the conditional expectation $mathbb{E}[f(X, Y,β) mid X]$, where $f$ is a nonlinear function and $β$ represents the decision variables. We focus on the practically important setting in which direct sampling from the conditional distribution of $Y mid X$ is infeasible, and only a stream […]