Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …

Estimating the unknown density from which a given independent sample originates is more
difficult than estimating the mean in the sense that, for the best popular nonparametric …

We propose a gradient-based simulated maximum likelihood estimation to estimate
unknown parameters in a stochastic model without assuming that the likelihood function of …

We present a gradient-based algorithm for solving a class of simulation optimization
problems in which the objective function is the quantile of a simulation output random …

Distortion risk measure, defined by an integral of a distorted tail probability, has been widely
used in behavioral economics and risk management as an alternative to expected utility …

We provide a unified framework to treat the asymptotic analysis for the non-batched quantile
sensitivity estimators of Fu et al.(2009), Liu and Hong (2009), and Lei et al.(2017). With only …

We propose a dynamic sampling allocation and selection paradigm for finding the
alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies …

Input models that drive stochastic simulations are often estimated from real-world samples of
data. This leads to uncertainty in the input models that propagates through to the simulation …

We propose a new unbiased stochastic gradient estimator for a family of stochastic models
driven by uniform random numbers as inputs. Dropping the requirement that the tails of the …

Mainstream reinforcement learning (RL) typically focuses on maximizing expected
cumulative rewards. In this paper, we explore a risk-sensitive RL setting where the objective …