Regret analysis for stochastic optimization problems under parametric models.

Abstract: In this talk, we analyze the growth rate of the regret -or optimality gap- when learning the optimal actions in stochastic optimization problems, formulated in a parametric setting. More precisely, we assume access to samples from random variables whose unknown distribution belongs to a parametric family. For both smooth and non-smooth problems, we describe the asymptotic behavior of the expected optimality gap, and use it to design appropriate estimators. Different examples will be given where explicit calculations are possible.

Posted on Nov 6, 2024 in Optimization and Equilibrium, Seminars