47 citations · 70 across the 10 of their papers we have counts for
10 papers
Dynamic Selection in Algorithmic Decision-making
Jin Li, Ye Luo, Xiaowei Zhang
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias (self-fulf…
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
Liang Ding, Rui Tuo, Xiaowei Zhang
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the…
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…
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
Jin Li, Ye Luo, Zigan Wang +1
In the standard data analysis framework, data is collected (once and for all), and then data analysis is carried out. However, with the advancement of digital technology, decision-…
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…
Distributionally Robust Selection of the Best
Weiwei Fan, L. Jeff Hong, Xiaowei Zhang
Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data re…
Affine Jump-Diffusions: Stochastic Stability and Limit Theorems
Xiaowei Zhang, Peter W. Glynn
Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. Me…
Scalable Stochastic Kriging with Markovian Covariances
Liang Ding, Xiaowei Zhang
Stochastic kriging is a popular technique for simulation metamodeling due to its exibility and analytical tractability. Its computational bottleneck is the inversion of a covarianc…
Stochastic Kriging for Inadequate Simulation Models
Lu Zou, Xiaowei Zhang
Stochastic kriging is a popular metamodeling technique for representing the unknown response surface of a simulation model. However, the simulation model may be inadequate in the s…
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…