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Enlu Zhou
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- affiliation: Georgia Institute of Technology, Atlanta, GA, USA
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2020 – today
- 2024
- [j30]Yingke Li, Mengxue Hou, Enlu Zhou, Fumin Zhang:
Dynamic event-triggered integrated task and motion planning for process-aware source seeking. Auton. Robots 48(8): 23 (2024) - [j29]Di Wu
, Yuhao Wang
Data-Driven Ranking and Selection Under Input Uncertainty. Oper. Res. 72(2): 781-795 (2024) - [j28]Tianyi Liu, Yifan Lin
Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data. SIAM J. Optim. 34(1): 389-418 (2024) - [j27]Yingke Li
A Distributed Bayesian Data Fusion Algorithm With Uniform Consistency. IEEE Trans. Autom. Control. 69(9): 6176-6182 (2024) - [j26]Sait Cakmak
Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation. ACM Trans. Model. Comput. Simul. 34(2): 8:1-8:24 (2024) - [c46]Enlu Zhou
Data-driven Simulation Optimization in the Age of Digital Twins. SIGSIM-PADS 2024: 2 - [i14]Yifan Lin, Yuhao Wang, Enlu Zhou:
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate. CoRR abs/2403.00675 (2024) - 2023
- [j25]Gongbo Zhang, Yijie Peng, Jianghua Zhang, Enlu Zhou:
Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives. INFORMS J. Comput. 35(6): 1261-1285 (2023) - [j24]Alexander Shapiro
Bayesian Distributionally Robust Optimization. SIAM J. Optim. 33(2): 1279-1304 (2023) - [c45]Yingke Li
Integrated Task and Motion Planning for Process-aware Source Seeking. ACC 2023: 527-532 - [c44]Yingke Li
Cognition Difference-Based Dynamic Trust Network for Distributed Bayesian Data Fusion. IROS 2023: 10933-10938 - [c43]Yuhao Wang, Enlu Zhou:
Bayesian Risk-Averse Q-Learning with Streaming Observations. NeurIPS 2023 - [c42]Yifan Lin, Enlu Zhou:
Reusing Historical Observations in Natural Policy Gradient. WSC 2023: 3071-3081 - [c41]Yuhao Wang, Enlu Zhou:
Input Data Collection Versus Simulation: Simultaneous Resource Allocation. WSC 2023: 3657-3668 - [i13]Yifan Lin, Enlu Zhou:
Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes. CoRR abs/2301.11415 (2023) - [i12]Yuhao Wang, Enlu Zhou:
Bayesian Risk-Averse Q-Learning with Streaming Observations. CoRR abs/2305.11300 (2023) - 2022
- [j23]Yingke Li
Bayesian Learning Model Predictive Control for Process-Aware Source Seeking. IEEE Control. Syst. Lett. 6: 692-697 (2022) - [c40]Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao:
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. AISTATS 2022: 2784-2802 - [c39]Yingke Li
Risk-Aware Model Predictive Control Enabled by Bayesian Learning. ACC 2022: 108-113 - [c38]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML 2022: 4831-4866 - [c37]Yifan Lin, Yuxuan Ren, Enlu Zhou:
Bayesian Risk Markov Decision Processes. NeurIPS 2022 - [c36]Yuhao Wang, Enlu Zhou:
Fixed Budget Ranking and Selection with Streaming Input Data. WSC 2022: 3027-3038 - [i11]Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao:
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. CoRR abs/2202.03535 (2022) - [i10]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR abs/2202.07549 (2022) - [i9]Yifan Lin, Yuhao Wang, Enlu Zhou:
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs. CoRR abs/2206.12463 (2022) - 2021
- [j22]Sait Cakmak
Solving Bayesian risk optimization via nested stochastic gradient estimation. IISE Trans. 53(10): 1081-1093 (2021) - [c35]Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao:
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. AISTATS 2021: 1891-1899 - [c34]Sait Cakmak, Enlu Zhou, Siyang Gao:
Contextual Ranking and Selection with Gaussian Processes. WSC 2021: 1-12 - [c33]Tianyi Liu, Yifan Lin, Enlu Zhou:
A Bayesian Approach to Online Simulation Optimization with Streaming Input Data. WSC 2021: 1-12 - [c32]Gongbo Zhang, Yijie Peng, Jianghua Zhang, Enlu Zhou:
Dynamic Sampling Policy For Subset Selection. WSC 2021: 1-12 - [i8]Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao:
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. CoRR abs/2102.12430 (2021) - [i7]Yifan Lin, Yuxuan Ren, Enlu Zhou:
A Bayesian Risk Approach to MDPs with Parameter Uncertainty. CoRR abs/2106.02558 (2021) - 2020
- [j21]Joshua Q. Hale, Helin Zhu, Enlu Zhou
Domination Measure: A New Metric for Solving Multiobjective Optimization. INFORMS J. Comput. 32(3): 565-581 (2020) - [j20]Helin Zhu, Tianyi Liu, Enlu Zhou
Risk Quantification in Stochastic Simulation under Input Uncertainty. ACM Trans. Model. Comput. Simul. 30(1): 1:1-1:24 (2020) - [c31]Sait Cakmak, Raul Astudillo, Peter I. Frazier, Enlu Zhou:
Bayesian Optimization of Risk Measures. NeurIPS 2020 - [c30]Tianyi Liu, Enlu Zhou:
Simulation Optimization by Reusing Past Replications: Don't Be Afraid of Dependence. WSC 2020: 2923-2934 - [c29]Yifan Lin, Enlu Zhou, Aly Megahed:
A Nested Simulation Optimization Approach for Portfolio Selection. WSC 2020: 3093-3104 - [i6]Sait Cakmak, Raul Astudillo, Peter I. Frazier, Enlu Zhou:
Bayesian Optimization of Risk Measures. CoRR abs/2007.05554 (2020)
2010 – 2019
- 2019
- [c28]Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao:
Toward Understanding the Importance of Noise in Training Neural Networks. ICML 2019: 7594-7602 - [c27]Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. NeurIPS 2019: 7890-7900 - [c26]Di Wu, Enlu Zhou:
Fixed Confidence Ranking and Selection Under Input Uncertainty. WSC 2019: 3717-3727 - [i5]Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Noise in Training Neural Networks. CoRR abs/1909.03172 (2019) - [i4]Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. CoRR abs/1909.04653 (2019) - 2018
- [j19]Enlu Zhou
Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space. INFORMS J. Comput. 30(1): 154-167 (2018) - [j18]Helin Zhu, Joshua Q. Hale, Enlu Zhou
Simulation optimization of risk measures with adaptive risk levels. J. Glob. Optim. 70(4): 783-809 (2018) - [j17]Jenny Jeong, Qinwei Zhuang
Experimental design and model reduction in systems biology. Quant. Biol. 6(4): 287-306 (2018) - [j16]Di Wu, Helin Zhu, Enlu Zhou
A Bayesian Risk Approach to Data-driven Stochastic Optimization: Formulations and Asymptotics. SIAM J. Optim. 28(2): 1588-1612 (2018) - [j15]Fan Ye
Weakly Coupled Dynamic Program: Information and Lagrangian Relaxations. IEEE Trans. Autom. Control. 63(3): 698-713 (2018) - [j14]Helin Zhu
Solving the Dual Problems of Dynamic Programs via Regression. IEEE Trans. Autom. Control. 63(5): 1340-1355 (2018) - [c25]Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao:
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. NeurIPS 2018: 3686-3696 - [c24]Enlu Zhou, Tianyi Liu:
Online Quantification of input uncertainty for parametric Models. WSC 2018: 1587-1598 - [c23]Di Wu, Enlu Zhou:
Provably Improving the Optimal Computing Budget Allocation Algorithm. WSC 2018: 1921-1932 - [i3]Tianyi Liu, Zhehui Chen, Enlu Zhou, Tuo Zhao:
Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations. CoRR abs/1802.05155 (2018) - [i2]Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao:
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. CoRR abs/1806.01660 (2018) - [i1]Di Wu, Enlu Zhou:
Analyzing and provably improving fixed budget ranking and selection algorithms. CoRR abs/1811.12183 (2018) - 2017
- [j13]Siyang Gao, Hui Xiao, Enlu Zhou, Weiwei Chen:
Robust ranking and selection with optimal computing budget allocation. Autom. 81: 30-36 (2017) - [j12]Joshua Q. Hale, Enlu Zhou
A Lagrangian search method for the P-median problem. J. Glob. Optim. 69(1): 137-156 (2017) - [j11]Henry Lam, Enlu Zhou:
The empirical likelihood approach to quantifying uncertainty in sample average approximation. Oper. Res. Lett. 45(4): 301-307 (2017) - [c22]Di Wu, Enlu Zhou:
Ranking and selection under input uncertainty: A budget allocation formulation. WSC 2017: 2245-2256 - 2016
- [c21]Theresa Roeder, Peter I. Frazier, Roberto Szechtman, Enlu Zhou:
Preface. WSC 2016: 1-4 - [c20]Helin Zhu, Joshua Q. Hale, Enlu Zhou:
Optimizing Conditional Value-at-Risk via gradient-based adaptive stochastic search. WSC 2016: 726-737 - [c19]Siyang Gao, Hui Xiao, Enlu Zhou, Weiwei Chen:
Optimal computing budget allocation with input uncertainty. WSC 2016: 839-846 - 2015
- [j10]Xi Chen, Enlu Zhou:
Population model-based optimization. J. Glob. Optim. 63(1): 125-148 (2015) - [j9]Fan Ye, Enlu Zhou:
Information Relaxation and Dual Formulation of Controlled Markov Diffusions. IEEE Trans. Autom. Control. 60(10): 2676-2691 (2015) - [c18]Yuan Yi, Wei Xie, Enlu Zhou:
A sequential experiment design for input uncertainty quantification in stochastic simulation. WSC 2015: 447-458 - [c17]Helin Zhu, Enlu Zhou:
Estimation of conditional value-at-risk for input uncertainty with budget allocation. WSC 2015: 655-666 - [c16]Joshua Q. Hale, Enlu Zhou:
A model-based approach to multi-objective optimization. WSC 2015: 3599-3609 - [c15]Enlu Zhou, Wei Xie:
Simulation optimization when facing input uncertainty. WSC 2015: 3714-3724 - [c14]Henry Lam, Enlu Zhou:
Quantifying uncertainty in sample average approximation. WSC 2015: 3846-3857 - 2014
- [j8]Enlu Zhou, Michael C. Fu, Steven I. Marcus:
Particle Filtering Framework for a Class of Randomized Optimization Algorithms. IEEE Trans. Autom. Control. 59(4): 1025-1030 (2014) - [j7]Enlu Zhou, Jiaqiao Hu:
Gradient-Based Adaptive Stochastic Search for Non-Differentiable Optimization. IEEE Trans. Autom. Control. 59(7): 1818-1832 (2014) - [j6]Jiaqiao Hu, Enlu Zhou, Qi Fan:
Model-Based Annealing Random Search with Stochastic Averaging. ACM Trans. Model. Comput. Simul. 24(4): 21:1-21:23 (2014) - [c13]Chang-han Rhee, Enlu Zhou, Peng Qiu:
An iterative algorithm for sampling from manifolds. WSC 2014: 574-585 - [c12]Enlu Zhou, Shalabh Bhatnagar
Simulation optimization via gradient-based stochastic search. WSC 2014: 3869-3879 - 2013
- [j5]Enlu Zhou, Xi Chen:
Sequential Monte Carlo simulated annealing. J. Glob. Optim. 55(1): 101-124 (2013) - [j4]Enlu Zhou:
Optimal Stopping Under Partial Observation: Near-Value Iteration. IEEE Trans. Autom. Control. 58(2): 500-506 (2013) - [j3]Fan Ye, Enlu Zhou:
Optimal Stopping of Partially Observable Markov Processes: A Filtering-Based Duality Approach. IEEE Trans. Autom. Control. 58(10): 2698-2704 (2013) - [c11]Helin Zhu, Fan Ye, Enlu Zhou:
True martingales for upper bounds on Bermudan option prices under jump-diffusion processes. WSC 2013: 113-124 - [c10]Xi Chen, Enlu Zhou:
Population model-based optimization with sequential Monte Carlo. WSC 2013: 1004-1015 - 2012
- [j2]Shen Yan, Enlu Zhou, Chun-Hung Chen:
Efficient Selection of a Set of Good Enough Designs With Complexity Preference. IEEE Trans Autom. Sci. Eng. 9(3): 596-606 (2012) - [c9]Fan Ye, Enlu Zhou:
Parameterized penalties in the dual representation of Markov decision processes. CDC 2012: 870-876 - [c8]Enlu Zhou, Jiaqiao Hu:
Combining gradient-based optimization with stochastic search. WSC 2012: 18:1-18:12 - 2011
- [c7]Fan Ye, Enlu Zhou:
Pricing American options under partial observation of stochastic volatility. WSC 2011: 3760-3771 - 2010
- [j1]Enlu Zhou, Michael C. Fu, Steven I. Marcus:
Solving Continuous-State POMDPs via Density Projection. IEEE Trans. Autom. Control. 55(5): 1101-1116 (2010) - [c6]Xi Chen, Enlu Zhou:
Simulation method for solving hybrid influence diagrams in decision making. WSC 2010: 383-392 - [c5]Shen Yan, Enlu Zhou, Chun-Hung Chen:
Efficient simulation budget allocation for selecting the best set of simplest good enough designs. WSC 2010: 1152-1159 - [c4]Enlu Zhou, Xi Chen:
A new population-based simulated annealing algorithm. WSC 2010: 1211-1222
2000 – 2009
- 2009
- [b1]Enlu Zhou:
Particle Filtering for Stochastic Control and Global Optimization. University of Maryland, College Park, MD, USA, 2009 - [c3]Enlu Zhou, Lin Kun, Michael C. Fu, Steven I. Marcus:
A Numerical Method for Financial Decision Problems under Stochastic Volatility. WSC 2009: 1299-1310 - 2008
- [c2]Enlu Zhou, Michael C. Fu, Steven I. Marcus:
A density projection approach to dimension reduction for continuous-state POMDPs. CDC 2008: 5576-5581 - [c1]Enlu Zhou, Michael C. Fu, Steven I. Marcus:
A particle filtering framework for randomized optimization algorithms. WSC 2008: 647-654
Coauthor Index
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last updated on 2024-12-10 21:47 CET by the dblp team
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