113 citations · 181 across the 8 of their papers we have counts for
9 papers
The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection
Zaile Li, Weiwei Fan, L. Jeff Hong
Ranking and selection (R&S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned tow…
Monte-Carlo Estimation of CoVaR
Weihuan Huang, Nifei Lin, L. Jeff Hong
is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being…
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
Tan Wan, L. Jeff Hong
Many large-scale production networks include thousands types of final products and tens to hundreds thousands types of raw materials and intermediate products. These networks face…
Surrogate-Based Simulation Optimization
L. Jeff Hong, Xiaowei Zhang
Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and…
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Wenhao Li, Ningyuan Chen, L. Jeff Hong
We consider a contextual online learning (multi-armed bandit) problem with high-dimensional covariate and decision . The reward function to learn, $f(\math…
Review on Ranking and Selection: A New Perspective
L. Jeff Hong, Weiwei Fan, Jun Luo
In this paper, we briefly review the development of ranking-and-selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the last…
Knowledge Gradient for Selection with Covariates: Consistency and Computation
Liang Ding, L. Jeff Hong, Haihui Shen +1
Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper we consider the ranking and selection pr…
Ranking and Selection with Covariates for Personalized Decision Making
Haihui Shen, L. Jeff Hong, Xiaowei Zhang
We consider a problem of ranking and selection via simulation in the context of personalized decision making, where the best alternative is not universal but varies as a function o…
Learning-based Robust Optimization: Procedures and Statistical Guarantees
L. Jeff Hong, Zhiyuan Huang, Henry Lam
Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions for optimization problems with uncertain constraints. In this paper, we study a statistical…