Publications

Book Chapter

  • Enlu Zhou and Di Wu, “Simulation Optimization under Input Model Uncertainty”, Advances in Modeling and Simulation: Seminal Research from 50 Years of Winter Simulation Conferences, Springer, 2017. Editors: Andreas Tolk, John Fowler, Guodong Shao, and Enver Yucesan.
  • Jiaqiao Hu and Enlu Zhou, “On the Implementation of a Class of Stochastic Search Algorithms”, pp. 427-435, Advances in Global Optimization, Springer Proceedings in Mathematics & Statistics, 2015.
  • Jiaqiao Hu, Yongqiang Wang, Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Survey of Some Model-Based Methods for Global Optimization”, pp. 157-180, Optimization, Control, and Applications of Stochastic Systems, in honor of Onésimo Hernández-Lerma, 2012. Editors: Hernández-Hernández, Daniel; Minjárez-Sosa, Adolfo.

Referred Journal Publications (* indicates a supervised student or postdoc co-author.)

Referred Conference Proceedings (* indicates a supervised student or postdoc co-author.)

  • Yuhao Wang* and Enlu Zhou, “Fixed Budget Ranking and Selection under Streaming Input Data”, Winter Simulation Conference, 2022. (WSC Best Theoretical Paper Award)
  • Yifan Lin*, Yuxuan Ren*, and Enlu Zhou, “Bayesian Risk Markov Decision Processes“, in Advances in Neural Information Processing Systems 36 (NeurIPS 2022) Proceedings, 2022.
  • Sam Daulton, Sait Cakmak*, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy, “Robust Multi-Objective Bayesian Optimization under Input Noise”, International Conference on Machine Learning (ICML), 2022.
  • Tianyi Liu*, Yan Li, Enlu Zhou, Tuo Zhao, “Noise Regularizes Over-Parameterized Rank One Matrix Recovery, Provably”, oral presentation, in Artificial Intelligence and Statistics (AISTATS), 2022.
  • Yingke Lin, Yifan Lin*, Enlu Zhou, and Fumin Zhang, “Risk-Aware Model Predictive Control Enabled by Bayesian Learning”, in Proceedings of American Control Conference, 2022.
  • Sait Cakmak*, Siyang Gao, and Enlu Zhou, “Contextual Ranking and Selection with Gaussian Processes”, Winter Simulation Conference, 2021.
  • Tianyi Liu*, Yifan Lin*, and Enlu Zhou, “A Bayesian Approach to Online Simulation Optimization with Streaming Input Data”, Winter Simulation Conference, 2021.
  • Yingke Li, Tianyi Liu*, Enlu Zhou, and Fumin Zhang, “Bayesian Learning Model Predictive Control for Process-Aware Source Seeking”, IEEE Conference on Decision and Control, 2021.
  • Tianyi Liu*, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao, “Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization”, in Artificial Intelligence and Statistics (AISTATS), 2021.
  • Sait Cakmak*, Rahul Astudillo, Peter Frazier, and Enlu Zhou, “Bayesian Optimization of Risk Measures“, in Advances in Neural Information Processing Systems 34 (NeurIPS 2020) Proceedings, 2020.
  • Tianyi Liu* and Enlu Zhou, “Simulation Optimization by Reusing Past Replicaitons: Don’t be afraid of Dependence”, in Proceedings of the 2020 Winter Simulation Conference, 2020. (WSC Best OR/MS-focused Student Paper Award)
  • Yifan Lin*, Enlu Zhou, and Aly Megahed, “A Nested Simulation Optimization Approach for Portfolio Selection”, in Proceedings of the 2020 Winter Simulation Conference, 2020.
  • Di Wu* and Enlu Zhou, “Fixed Confidence Ranking and Selection under Input Uncertainty”, in Proceedings of the 2019 Winter Simulation Conference, 2019.
  • Tianyi Liu*, MInshuo Chen, Mo Zhou, Simon Du, Enlu Zhou, and Tuo Zhao, “Towards Understanding the Importance of Shortcut Connections in Residual Networks”, in Advances in Neural Information Processing Systems 33 (NeurIPS 2019) Proceedings, 2019.
  • Mo Zhou, Tianyi Liu*, Yan Li, Dachao Lin, Enlu Zhou, and Tuo Zhao, “Towards Understanding the Importance of Noise in Training Neural Networks”, in International Conference on Machine Learning (ICML) Proceedings, 2019.
  • Tianyi Liu*, Shiyang Li, Jianping Shi, Enlu Zhou, and Tuo Zhao,”Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization“, in Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Proceedings, 2018.
  • Enlu Zhou, and Tianyi Liu*, “Online Quantification of Input Uncertainty for Parametric Models”, in Proceedings of the 2018 Winter Simulation Conference, 2018.
  • Di Wu*, and Enlu Zhou, “Analyzing and Provably Improving the Optimal Computing Budget Allocation Algorithm”, in Proceedings of the 2018 Winter Simulation Conference, 2018.
  • Di Wu*, and Enlu Zhou, “Ranking and Selection under Input Uncertainty: a Budget Allocation Formulation”, in Proceedings of the 2017 Winter Simulation Conference, 2017.
  • Helin Zhu*, Fan Ye* and Enlu Zhou, “An Efficient Regression Approach to Solving the Dual Problems of Dynamic Programs”, in Proceedings of the 20th IFAC World Congress, 2017.
  • Helin Zhu*, Joshua Hale*, and Enlu Zhou, “Optimizing Conditional Value-at-Risk via Gradient-based Adaptive Stochastic Search”, in Proceedings of the 2016 Winter Simulation Conference, 2016.
  • Siyang Gao, Hui Xiao, Enlu Zhou, and Weiwei Chen, “Optimal Computing Budget Allocation with Input Uncertainty”, in Proceedings of the 2016 Winter Simulation Conference, 2016.
  • Enlu Zhou and Wei Xie, “Simulation Optimizaion when Facing Input Uncertainty“, in Proceedings of the 2015 Winter Simulation Conference, 2015.
  • Henry Lam and Enlu Zhou, “Quantifying Uncertainty in Sample Average Approximation“, in Proceedings of the 2015 Winter Simulation Conference, 2015.
  • Helin Zhu* and Enlu Zhou, “Risk Assessment for Input Uncertainty with Budget Allocation”, in Proceedings of the 2015 Winter Simulation Conference, 2015.
  • Joshua Hale* and Enlu Zhou, “A Model-based Approach to Multi-objective Optimization”, in Proceedings of the 2015 Winter Simulation Conference, 2015.
  • Yi Yuan, Wei Xie, and Enlu Zhou, “A Sequential Experiment Design for Input Uncertainty Quantification in Stochastic Simulation”, in Proceedings of the 2015 Winter Simulation Conference, 2015.
  • Chang-Han Rhee*, Enlu Zhou, and Peng Qiu, “An Iterative Algorithm for Sampling from Manifolds“, in Proceedings of the 2014 Winter Simulation Conference, 2014.
  • Enlu Zhou, Shalabh Bhatnagar, and Xi Chen*, “Simulation Optimization via Gradient-based Stochastic Search“, in Proceedings of the 2014 Winter Simulation Conference, 2014.
  • Fan Ye* and Enlu Zhou, “Dual Formulation of Controlled Markov Diffusions and Its Application”, in Proceedings of the 19th IFAC World Congress, 2014.
  • Xi Chen* and Enlu Zhou, “Population Model-based Optimization with Sequential Monte Carlo”, in Proceedings of the 2013 Winter Simulation Conference, 2013.
  • Helin Zhu*, Fan Ye*, and Enlu Zhou, “True Upper Bounds on Bermudan Option Prices Under Jump-diffusion Processes”, in Proceedings of the 2013 Winter Simulation Conference, 2013.
  • Enlu Zhou and Jiaqiao Hu, “Combining Gradient-based Optimization with Stochastic Search”, in Proceedings of the 2012 Winter Simulation Conference.
  • Fan Ye* and Enlu Zhou, “Parameterized Penalties in the Dual Representation of Markov Decision Processes”, in Proceedings of the 51stIEEE Conference on Decision and Control, 2012.
  • Fan Ye* and Enlu Zhou, “Pricing American Options under Partial Observation of Stochastic Volatility”, in Proceedings of the 2011 Winter Simulation Conference, 2011.
  • Shen Yan*, Enlu Zhou, and Chun-Hung Chen, “Efficient Simulation Budget Allocation for Selecting the Best Set of Simplest Good Enough Designs”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
  • Enlu Zhou and Xi Chen*, “A New Population-Based Simulated Annealing Algorithm”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
  • Xi Chen* and Enlu Zhou, “Simulation Method for Solving Hybrid Influence Diagrams in Decision Making”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
  • Enlu Zhou, Kun Lin, Michael C. Fu, and Steven I. Marcus, “A Numerical Method for Financial Decision Problems under Stochastic Volatility”, in Proceedings of the 2009 Winter Simulation Conference, 2009. (WSC Best Theoretical Paper Award)
  • Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Particle Filtering Framework for Randomized Optimization Algorithms”, in Proceedings of the 2008 Winter Simulation Conference, 2008. (Finalist for the “Best Student Paper” Award)
  • Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Density Projection Approach to Dimension Reduction for Continuous-State POMDPs”, in Proceedings of 47th IEEE Conference on Decision and Control, 2008.