From the 1 of 6 linked papers with an AI index.
6 papers
Asymptotic Analysis of Empirical Dynamic Programming in Infinite-Horizon Stochastic Optimal Control
Xin Chen, Elif Sena Isik, Johannes Milz
We derive statistical limit theorems for sample-based approximations of infinite-horizon discounted stochastic optimal control problems in discrete time. Our first result is a func…
Statistical Inference for Scenario-Based Dynamic Optimization under Uncertainty
Aurya Javeed, Johannes Milz
The paper develops statistical methods for evaluating and constructing confidence intervals for the optimal value of finite-horizon open-loop dynamic optimization problems when par…
Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
Johannes Milz, Alexander Shapiro
We establish central limit theorems for the Sample Average Approximation (SAA) method in discrete-time, finite-horizon stochastic optimal control. Our analysis is based on an abstr…
Stochastic Optimal Control with Side Information and Bayesian Learning
Johannes Milz, Alexander Shapiro, Enlu Zhou
We study infinite-horizon stochastic optimal control problems with observable side information: a Markov chain that modulates an unknown context-conditional randomness distribution…
Convergence rates for ensemble-based solutions to optimal control of uncertain dynamical systems
Olena Melnikov, Johannes Milz
We consider optimal control problems involving nonlinear ordinary differential equations with uncertain inputs. Using the sample average approximation, we obtain optimal control pr…
Nonparametric Robust Comparison of Solutions under Input Uncertainty
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song
We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIO…