We propose estimators and analyze their large-sample distribution for two problems, data-oriented expected performance evaluation and data-oriented stochastic optimization, in presence of a specific class of non-stationarities in the observed data. The considered non-stationarities reflect...

We study an infinite-horizon cumulative innovation model, in which patents are characterized bytheir length and the likelihood of being ruled valid in a patent infringement litigation. Strengtheningpatent protection via a greater validity or length has two opposing effects on innovation: 1)...

We design new policies that ensure both worst-case optimality for expected regret and light-tailed risk for regret distribution in the stochastic multi-armed bandit problem. Recently, \cite{fan2021fragility} showed that information-theoretically optimized bandit algorithms suffer from some...

We propose a new framework named DS-WGAN that integrates the doubly stochastic (DS) structure and the Wasserstein generative adversarial networks (WGAN) to model, estimate, and simulate a wide class of arrival processes with general non-stationary and random arrival rates. Regarding statistical...

We propose a set of new algorithms based on stochastic localization methods for large-scale discrete simulation optimization problems with convexity structure. All proposed algorithms, with the general idea of "localizing" potential good solutions to an adaptively shrinking subset, are...

Simulation is often used to evaluate and compare performances of stochastic systems, where the underlying stochastic models are estimated from real-world input data. Collecting more input data can derive closer-to-reality stochastic models while generating more simulation replications can reduce...

In real-time decision-making problems for complicated stochastic systems, a covariate that reflects the state of the system is observed in real time and a state-dependent decision needs to be made immediately to optimize some system performance. Such system performances, for complicated...