We review simulation optimization methods and discuss how these methods underpin
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …

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 …

Gradient-based saliency maps are widely used to explain deep neural network decisions.
However, as models become deeper and more black-box, such as in closed-source APIs …

We investigate a new approach to compute the gradients of artificial neural networks
(ANNs), based on the so-called push-out likelihood ratio method. Unlike the widely used …

Modeling dependence among input random variables is often critical for performance
evaluation of stochastic systems, and copulas provide one approach to model such …

Conditional value at risk (CVaR) is a popular measure for quantifying portfolio risk.
Sensitivity analysis of CVaR is common in risk management and gradient-based …

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 …

Automatic differentiation (AD) can provide infinitesimal perturbation analysis (IPA) derivative
estimates directly from simulation code. These gradient estimators are simple to obtain …

We consider a class of unbiased Monte Carlo estimators and develop an efficient algorithm
to produce the distribution of the optimal random truncation level. We establish the …

Policy gradient methods are successful for a wide range of reinforcement learning tasks.
Traditionally, such methods utilize the score function as stochastic gradient estimator. We …