A density projection approach to dimension reduction for continuous-state POMDPs. - Enlu Zhou; Michael C. Fu 001; Steven I. Marcus (CDC, 2008)

A particle filtering framework for randomized optimization algorithms. - Enlu Zhou; Michael C. Fu 001; Steven I. Marcus (WSC, 2008)

A Numerical Method for Financial Decision Problems under Stochastic Volatility. - Enlu Zhou; Lin Kun; Michael C. Fu 001; Steven I. Marcus (WSC, 2009)

Particle Filtering for Stochastic Control and Global Optimization. - Enlu Zhou (2009)

Simulation method for solving hybrid influence diagrams in decision making. - Xi Chen; Enlu Zhou (WSC, 2010)

Efficient simulation budget allocation for selecting the best set of simplest good enough designs. - Shen Yan; Enlu Zhou; Chun-Hung Chen (WSC, 2010)

A new population-based simulated annealing algorithm. - Enlu Zhou; Xi Chen (WSC, 2010)

Solving Continuous-State POMDPs via Density Projection. - Enlu Zhou; Michael C. Fu 001; Steven I. Marcus (IEEE Trans. Autom. Control., 2010)

Pricing American options under partial observation of stochastic volatility. - Fan Ye 004; Enlu Zhou (WSC, 2011)

Parameterized penalties in the dual representation of Markov decision processes. - Fan Ye 004; Enlu Zhou (CDC, 2012)

Combining gradient-based optimization with stochastic search. - Enlu Zhou; Jiaqiao Hu (WSC, 2012)

Efficient Selection of a Set of Good Enough Designs With Complexity Preference. - Shen Yan; Enlu Zhou; Chun-Hung Chen (IEEE Trans Autom. Sci. Eng., 2012)

Population model-based optimization with sequential Monte Carlo. - Xi Chen; Enlu Zhou (WSC, 2013)

True martingales for upper bounds on Bermudan option prices under jump-diffusion processes. - Helin Zhu; Fan Ye 004; Enlu Zhou (WSC, 2013)

Sequential Monte Carlo simulated annealing. - Enlu Zhou; Xi Chen (J. Glob. Optim., 2013)

Optimal Stopping of Partially Observable Markov Processes: A Filtering-Based Duality Approach. - Fan Ye 004; Enlu Zhou (IEEE Trans. Autom. Control., 2013)

Optimal Stopping Under Partial Observation: Near-Value Iteration. - Enlu Zhou (IEEE Trans. Autom. Control., 2013)

An iterative algorithm for sampling from manifolds. - Chang-han Rhee; Enlu Zhou; Peng Qiu (WSC, 2014)

Simulation optimization via gradient-based stochastic search. - Enlu Zhou; Shalabh Bhatnagar; Xi Chen (WSC, 2014)

Particle Filtering Framework for a Class of Randomized Optimization Algorithms. - Enlu Zhou; Michael C. Fu 001; Steven I. Marcus (IEEE Trans. Autom. Control., 2014)

Gradient-Based Adaptive Stochastic Search for Non-Differentiable Optimization. - Enlu Zhou; Jiaqiao Hu (IEEE Trans. Autom. Control., 2014)

Model-Based Annealing Random Search with Stochastic Averaging. - Jiaqiao Hu; Enlu Zhou; Qi Fan (ACM Trans. Model. Comput. Simul., 2014)

A model-based approach to multi-objective optimization. - Joshua Q. Hale; Enlu Zhou (WSC, 2015)

Quantifying uncertainty in sample average approximation. - Henry Lam; Enlu Zhou (WSC, 2015)

A sequential experiment design for input uncertainty quantification in stochastic simulation. - Yuan Yi; Wei Xie; Enlu Zhou (WSC, 2015)

Simulation optimization when facing input uncertainty. - Enlu Zhou; Wei Xie (WSC, 2015)

Estimation of conditional value-at-risk for input uncertainty with budget allocation. - Helin Zhu; Enlu Zhou (WSC, 2015)

Population model-based optimization. - Xi Chen; Enlu Zhou (J. Glob. Optim., 2015)

Information Relaxation and Dual Formulation of Controlled Markov Diffusions. - Fan Ye 004; Enlu Zhou (IEEE Trans. Autom. Control., 2015)

Optimal computing budget allocation with input uncertainty. - Siyang Gao; Hui Xiao 001; Enlu Zhou; Weiwei Chen 003 (WSC, 2016)

Preface. - Theresa Roeder; Peter I. Frazier; Roberto Szechtman; Enlu Zhou (WSC, 2016)

Optimizing Conditional Value-at-Risk via gradient-based adaptive stochastic search. - Helin Zhu; Joshua Q. Hale; Enlu Zhou (WSC, 2016)

Ranking and selection under input uncertainty: A budget allocation formulation. - Di Wu 034; Enlu Zhou (WSC, 2017)

Robust ranking and selection with optimal computing budget allocation. - Siyang Gao; Hui Xiao 001; Enlu Zhou; Weiwei Chen 003 (Autom., 2017)

A Lagrangian search method for the P-median problem. - Joshua Q. Hale; Enlu Zhou; Jiming Peng (J. Glob. Optim., 2017)

The empirical likelihood approach to quantifying uncertainty in sample average approximation. - Henry Lam; Enlu Zhou (Oper. Res. Lett., 2017)

Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. - Tianyi Liu; Shiyang Li; Jianping Shi; Enlu Zhou; Tuo Zhao (NeurIPS, 2018)

Provably Improving the Optimal Computing Budget Allocation Algorithm. - Di Wu 034; Enlu Zhou (WSC, 2018)

Online Quantification of input uncertainty for parametric Models. - Enlu Zhou; Tianyi Liu (WSC, 2018)

Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations. - Tianyi Liu; Zhehui Chen; Enlu Zhou; Tuo Zhao (CoRR, 2018)

Analyzing and provably improving fixed budget ranking and selection algorithms. - Di Wu 034; Enlu Zhou (CoRR, 2018)

Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space. - Enlu Zhou; Shalabh Bhatnagar (INFORMS J. Comput., 2018)

Simulation optimization of risk measures with adaptive risk levels. - Helin Zhu; Joshua Q. Hale; Enlu Zhou (J. Glob. Optim., 2018)

Experimental design and model reduction in systems biology. - Jenny Jeong; Qinwei Zhuang; Mark K. Transtrum; Enlu Zhou; Peng Qiu (Quant. Biol., 2018)

A Bayesian Risk Approach to Data-driven Stochastic Optimization: Formulations and Asymptotics. - Di Wu 034; Helin Zhu; Enlu Zhou (SIAM J. Optim., 2018)

Weakly Coupled Dynamic Program: Information and Lagrangian Relaxations. - Fan Ye 004; Helin Zhu; Enlu Zhou (IEEE Trans. Autom. Control., 2018)

Solving the Dual Problems of Dynamic Programs via Regression. - Helin Zhu; Fan Ye 004; Enlu Zhou (IEEE Trans. Autom. Control., 2018)

Toward Understanding the Importance of Noise in Training Neural Networks. - Mo Zhou; Tianyi Liu; Yan Li 074; Dachao Lin; Enlu Zhou; Tuo Zhao (ICML, 2019)

Towards Understanding the Importance of Shortcut Connections in Residual Networks. - Tianyi Liu; Minshuo Chen; Mo Zhou; Simon S. Du; Enlu Zhou; Tuo Zhao (NeurIPS, 2019)

Fixed Confidence Ranking and Selection Under Input Uncertainty. - Di Wu 034; Enlu Zhou (WSC, 2019)

Towards Understanding the Importance of Noise in Training Neural Networks. - Mo Zhou; Tianyi Liu; Yan Li 074; Dachao Lin; Enlu Zhou; Tuo Zhao (CoRR, 2019)

Bayesian Optimization of Risk Measures. - Sait Cakmak; Raul Astudillo; Peter I. Frazier; Enlu Zhou (NeurIPS, 2020)

A Nested Simulation Optimization Approach for Portfolio Selection. - Yifan Lin; Enlu Zhou; Aly Megahed (WSC, 2020)

Simulation Optimization by Reusing Past Replications: Don't Be Afraid of Dependence. - Tianyi Liu; Enlu Zhou (WSC, 2020)

Domination Measure: A New Metric for Solving Multiobjective Optimization. - Joshua Q. Hale; Helin Zhu; Enlu Zhou (INFORMS J. Comput., 2020)

Risk Quantification in Stochastic Simulation under Input Uncertainty. - Helin Zhu; Tianyi Liu; Enlu Zhou (ACM Trans. Model. Comput. Simul., 2020)

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. - Tianyi Liu; Yan Li 074; Song Wei; Enlu Zhou; Tuo Zhao (AISTATS, 2021)

Contextual Ranking and Selection with Gaussian Processes. - Sait Cakmak; Enlu Zhou; Siyang Gao (WSC, 2021)

A Bayesian Approach to Online Simulation Optimization with Streaming Input Data. - Tianyi Liu; Yifan Lin; Enlu Zhou (WSC, 2021)

Dynamic Sampling Policy For Subset Selection. - Gongbo Zhang; Yijie Peng; Jianghua Zhang; Enlu Zhou (WSC, 2021)

A Bayesian Risk Approach to MDPs with Parameter Uncertainty. - Yifan Lin; Yuxuan Ren; Enlu Zhou (CoRR, 2021)

Solving Bayesian risk optimization via nested stochastic gradient estimation. - Sait Cakmak; Di Wu 034; Enlu Zhou (IISE Trans., 2021)

Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. - Tianyi Liu; Yan Li 074; Enlu Zhou; Tuo Zhao (AISTATS, 2022)

Risk-Aware Model Predictive Control Enabled by Bayesian Learning. - Yingke Li; Yifan Lin; Enlu Zhou; Fumin Zhang 001 (ACC, 2022)

Robust Multi-Objective Bayesian Optimization Under Input Noise. - Samuel Daulton; Sait Cakmak; Maximilian Balandat; Michael A. Osborne; Enlu Zhou; Eytan Bakshy (ICML, 2022)

Bayesian Risk Markov Decision Processes. - Yifan Lin; Yuxuan Ren; Enlu Zhou (NeurIPS, 2022)

Fixed Budget Ranking and Selection with Streaming Input Data. - Yuhao Wang; Enlu Zhou (WSC, 2022)

Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs. - Yifan Lin; Yuhao Wang; Enlu Zhou (CoRR, 2022)

Bayesian Learning Model Predictive Control for Process-Aware Source Seeking. - Yingke Li; Tianyi Liu; Enlu Zhou; Fumin Zhang 001 (IEEE Control. Syst. Lett., 2022)

Integrated Task and Motion Planning for Process-aware Source Seeking. - Yingke Li; Mengxue Hou; Enlu Zhou; Fumin Zhang 001 (ACC, 2023)

Cognition Difference-Based Dynamic Trust Network for Distributed Bayesian Data Fusion. - Yingke Li; Ziqiao Zhang; Junkai Wang; Huibo Zhang; Enlu Zhou; Fumin Zhang 001 (IROS, 2023)

Bayesian Risk-Averse Q-Learning with Streaming Observations. - Yuhao Wang; Enlu Zhou (NeurIPS, 2023)

Reusing Historical Observations in Natural Policy Gradient. - Yifan Lin; Enlu Zhou (WSC, 2023)

Input Data Collection Versus Simulation: Simultaneous Resource Allocation. - Yuhao Wang; Enlu Zhou (WSC, 2023)

Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives. - Gongbo Zhang; Yijie Peng; Jianghua Zhang; Enlu Zhou (INFORMS J. Comput., 2023)

Bayesian Distributionally Robust Optimization. - Alexander Shapiro 001; Enlu Zhou; Yifan Lin (SIAM J. Optim., 2023)

Data-driven Simulation Optimization in the Age of Digital Twins. - Enlu Zhou (SIGSIM-PADS, 2024)

Selection of the Best System With an Optimized Continuous Variable. - Yuhao Wang; Seong-Hee Kim; Enlu Zhou (WSC, 2024)

Data-Driven Simulation Optimization in the Age of Digital Twins: Challenges and Developments. - Enlu Zhou (WSC, 2024)

Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate. - Yifan Lin; Yuhao Wang; Enlu Zhou (CoRR, 2024)

Data-Driven Ranking and Selection Under Input Uncertainty. - Di Wu 034; Yuhao Wang; Enlu Zhou (Oper. Res., 2024)

Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data. - Tianyi Liu; Yifan Lin; Enlu Zhou (SIAM J. Optim., 2024)

Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation. - Sait Cakmak; Yuhao Wang; Siyang Gao; Enlu Zhou (ACM Trans. Model. Comput. Simul., 2024)

A Distributed Bayesian Data Fusion Algorithm With Uniform Consistency. - Yingke Li; Enlu Zhou; Fumin Zhang 001 (IEEE Trans. Autom. Control., 2024)

Dynamic event-triggered integrated task and motion planning for process-aware source seeking. - Yingke Li; Mengxue Hou; Enlu Zhou; Fumin Zhang 001 (Auton. Robots, 2024)

Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes. - Yifan Lin; Enlu Zhou (AAAI, 2025)

Optimal Computing Budget Allocation for Data-Driven Ranking and Selection. - Yuhao Wang; Enlu Zhou (INFORMS J. Optim., 2025)

Mori-zwanzig approach for belief abstraction with application to belief space planning. - Mengxue Hou; Tony X. Lin; Enlu Zhou; Fumin Zhang 001 (Auton. Robots, 2025)

Ranking and Selection with Simultaneous Input Data Collection. - Yuhao Wang; Enlu Zhou (CoRR, 2025)