Motivated by stochastic optimization, we introduce the problem of learning from samples of contextual value distributions. A contextual value distribution can be understood as a family of real-valued distributions, where each sample consists of a context x and a random variable drawn from the corresponding real-valued distribution Dx. By minimizing a convex surrogate loss, we learn an empirical distribution D'x for each context, ensuring a small Levy distance to Dx.