AI RESEARCH
Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization
arXiv CS.LG
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ArXi:2603.13048v1 Announce Type: cross 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 $\mathbb{E}[f(X, Y,\beta) \mid X]$, where $f$ is a nonlinear function and $\beta$ represents the decision variables.