The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

Learning models with uniform performance via distributionally robust optimization Page 1 The
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …

Reinforcement learning (RL) has demonstrated remarkable achievements in simulated
environments. However, carrying this success to real environments requires the important …

We consider a reinforcement learning setting in which the deployment environment is
different from the training environment. Applying a robust Markov decision processes …

Policy learning using historical observational data is an important problem that has found
widespread applications. However, existing literature rests on the crucial assumption that …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

Policy learning using historical observational data are an important problem that has
widespread applications. Examples include selecting offers, prices, or advertisements for …

Some recent works showed that several machine learning algorithms, such as square-root
Lasso, Support Vector Machines, and regularized logistic regression, among many others …

Discrete-event simulation models generate random variates from input distributions and
compute outputs according to the simulation logic. The input distributions are typically fitted …