| Ranking and selection with covariates for personalized decision making H Shen, LJ Hong, X Zhang INFORMS Journal on Computing 33 (4), 1500-1519, 2021 | 59 | 2021 |
| Distributionally robust selection of the best W Fan, LJ Hong, X Zhang Management Science 66 (1), 190-208, 2020 | 40 | 2020 |
| Affine point processes: Approximation and efficient simulation X Zhang, J Blanchet, K Giesecke, PW Glynn Mathematics of Operations Research 40 (4), 797 - 819, 2015 | 36 | 2015 |
| Robust selection of the best W Fan, LJ Hong, X Zhang Proceedings of the 2013 Winter Simulation Conference, 868 - 876, 2013 | 32 | 2013 |
| Knowledge gradient for selection with covariates: Consistency and computation L Ding, LJ Hong, H Shen, X Zhang Naval Research Logistics 69 (3), 496-507, 2022 | 27 | 2022 |
| Rare event simulation for a generalized Hawkes process X Zhang, PW Glynn, K Giesecke, J Blanchet Proceedings of the 2009 Winter Simulation Conference, 1291-1298, 2009 | 25 | 2009 |
| Scaling and modeling of call center arrivals X Zhang, LJ Hong, J Zhang Proceedings of the 2014 Winter Simulation Conference, 476 - 485, 2014 | 20 | 2014 |
| Enhancing stochastic kriging for queueing simulation with stylized models H Shen, LJ Hong, X Zhang IISE Transactions 50 (11), 943-958, 2018 | 18 | 2018 |
| Causal reinforcement learning: An instrumental variable approach J Li, Y Luo, X Zhang Available at SSRN: https://ssrn.com/abstract=3792824, 2021 | 17 | 2021 |
| Ranking and selection with covariates H Shen, LJ Hong, X Zhang Proceedings of the 2017 Winter Simulation Conference, 2137-2148, 2017 | 16 | 2017 |
| Surrogate-based simulation optimization LJ Hong, X Zhang Emerging Optimization Methods and Modeling Techniques with Applications, 287–311, 2021 | 15 | 2021 |
| Affine jump-diffusions: Stochastic stability and limit theorems X Zhang, PW Glynn Available at arXiv: https://arxiv.org/abs/1811.00122, 2018 | 15 | 2018 |
| Data-driven ranking and selection: High-dimensional covariates and general dependence X Li, X Zhang, Z Zheng Proceedings of the 2018 Winter Simulation Conference, 1933-1944, 2018 | 11 | 2018 |
| On the dynamics of a finite buffer queue conditioned on the amount of loss X Zhang, PW Glynn Queueing Systems: Theory and Applications 67 (2), 91-110, 2011 | 9 | 2011 |
| Sample and computationally efficient stochastic kriging in high dimensions L Ding, X Zhang Operations Research 72 (2), 660-683, 2024 | 8* | 2024 |
| Sequential sampling for Bayesian robust ranking and selection X Zhang, L Ding Proceedings of the 2016 Winter Simulation Conference, 758-769, 2016 | 8 | 2016 |
| Scalable stochastic kriging with Markovian covariances L Ding, X Zhang Available at arXiv: https://arxiv.org/abs/1803.02575, 2018 | 5 | 2018 |
| A Bayesian approach for modeling and analysis of call center arrivals X Zhang Proceedings of the 2013 Winter Simulation Conference, 713 - 723, 2013 | 5 | 2013 |
| Smooth nested simulation: Bridging cubic and square root convergence rates in high dimensions W Wang, Y Wang, X Zhang Management Science, 2024 | 4 | 2024 |
| Stochastic kriging for inadequate simulation models L Zou, X Zhang Available at arXiv: https://arxiv.org/abs/1802.00677, 2018 | 4 | 2018 |
