A dynamic framework for statistical selection problems. - Yijie Peng; Michael C. Fu 001; Chun-Hung Chen; Jian-Qiang Hu (WSC, 2013)

Efficient Simulation Resource Sharing and Allocation for Selecting the Best. - Yijie Peng; Chun-Hung Chen; Michael C. Fu 001; Jian-Qiang Hu (IEEE Trans. Autom. Control., 2013)

Non-monotonicity of probability of correct selection. - Yijie Peng; Chun-Hung Chen; Michael C. Fu 001; Jian-Qiang Hu (WSC, 2015)

Estimating distribution sensitivity using generalized likelihood ratio method. - Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu (WODES, 2016)

On the regularity conditions and applications for generalized likelihood ratio method. - Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu (WSC, 2016)

Dynamic Sampling Allocation and Design Selection. - Yijie Peng; Chun-Hung Chen; Michael C. Fu 001; Jian-Qiang Hu (INFORMS J. Comput., 2016)

An optimization approach for team coordination through information sharing. - Yijie Peng; Edward Huang; Jie Xu 004; Chun-Hung Chen (CASE, 2017)

On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation. - Yijie Peng; Michael C. Fu 001; Peter W. Glynn; Jianqiang Hu (WSC, 2017)

Myopic Allocation Policy With Asymptotically Optimal Sampling Rate. - Yijie Peng; Michael C. Fu 001 (IEEE Trans. Autom. Control., 2017)

Efficient Sampling Procedure for Selecting the Largest Stationary Probability of a Markov Chain. - Haidong Li; Xiaoyun Xu; Yijie Peng; Chun-Hung Chen (CASE, 2018)

Dynamic Sampling for Feasibility Determination. - Yijie Peng; Jie Song 002; Jie Xu 004; Edwin K. P. Chong (CASE, 2018)

A Review of Static and Dynamic Optimization for Ranking and Selection. - Yijie Peng; Chun-Hung Chen; Edwin K. P. Chong; Michael C. Fu 001 (WSC, 2018)

Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation. - Lei Lei 005; Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu (Discret. Event Dyn. Syst., 2018)

A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters. - Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu; Bernd Heidergott (Oper. Res., 2018)

Ranking and Selection as Stochastic Control. - Yijie Peng; Edwin K. P. Chong; Chun-Hung Chen; Michael C. Fu 001 (IEEE Trans. Autom. Control., 2018)

Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario. - Yijie Peng; Chun-Hung Chen; Michael C. Fu 001; Jian-Qiang Hu (IEEE Trans. Autom. Control., 2018)

optimizing outpatient Department Staffing Level using Multi-Fidelity Models. - Bowen Pang; Xiaolei Xie; Bernd Heidergott; Yijie Peng (CASE, 2019)

Dynamic Sampling Procedure for Decomposable Random Networks. - Haidong Li; Yijie Peng; Xiaoyun Xu; Chun-Hung Chen; Bernd Heidergott (WSC, 2019)

Estimating Quantile Sensitivity for Financial Models with Correlations and Jumps. - Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu; Lei Lei 005 (WSC, 2019)

From Data to Stochastic Modeling and Decision Making: What Can We Do Better? - Joost Berkhout; Bernd Heidergott; Henry Lam; Yijie Peng (Asia Pac. J. Oper. Res., 2019)

Preface. - Yijie Peng (Asia Pac. J. Oper. Res., 2019)

Efficient Sampling for Selecting Important Nodes in Random Network. - Haidong Li; Xiaoyun Xu; Yijie Peng; Chun-Hung Chen (CoRR, 2019)

Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Adversarial Defensiveness. - Li Xiao 005; Yijie Peng; L. Jeff Hong; Zewu Ke (CoRR, 2019)

A Coordinate Optimization Approach for Concurrent Design. - Yijie Peng; Edward Huang; Jie Xu 004; Zhongshun Shi; Chun-Hung Chen (IEEE Trans. Autom. Control., 2019)

Efficient Simulation Sampling Allocation Using Multifidelity Models. - Yijie Peng; Jie Xu 004; Loo Hay Lee; Jianqiang Hu; Chun-Hung Chen (IEEE Trans. Autom. Control., 2019)

Training Artificial Neural Networks by Generalized Likelihood Ratio Method: An Effective Way to Improve Robustness. - Li Xiao 005; Yijie Peng; L. Jeff Hong; Zewu Ke; Shuhuai Yang (CASE, 2020)

Dynamic Sampling Allocation for Selecting a Good Enough Alternative. - Gongbo Zhang; Chun-Hung Chen; Qing-Shan Jia; Yijie Peng (CASE, 2020)

Confidence Intervals and Regions for Quantiles using Conditional Monte Carlo and Generalized Likelihood Ratios. - Lei Lei 005; Christos Alexopoulos; Yijie Peng; James R. Wilson (WSC, 2020)

Context-Dependent Ranking and Selection under a Bayesian Framework. - Haidong Li; Henry Lam; Zhe Liang; Yijie Peng (WSC, 2020)

Asynchronous Value Iteration for Markov Decision Processes with Continuous State Spaces. - Xiangyu Yang; Jian-Qiang Hu; Jiaqiao Hu; Yijie Peng (WSC, 2020)

Sequential Sampling for a Ranking and Selection Problem with Exponential Sampling Distributions. - Gongbo Zhang; Haidong Li; Yijie Peng (WSC, 2020)

Optimal unbiased estimation for expected cumulative discounted cost. - Zhenyu Cui; Michael C. Fu 001; Yijie Peng; Lingjiong Zhu (Eur. J. Oper. Res., 2020)

On the Variance of Single-Run Unbiased Stochastic Derivative Estimators. - Zhenyu Cui; Michael C. Fu 001; Jian-Qiang Hu; Yanchu Liu; Yijie Peng; Lingjiong Zhu (INFORMS J. Comput., 2020)

Technical Note - Central Limit Theorems for Estimated Functions at Estimated Points. - Peter W. Glynn; Lin Fan; Michael C. Fu 001; Jian-Qiang Hu; Yijie Peng (Oper. Res., 2020)

Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling. - Yijie Peng; Michael C. Fu 001; Bernd Heidergott; Henry Lam (Oper. Res., 2020)

Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation. - Yijie Peng; Jie Song 002; Jie Xu 004; Edwin K. P. Chong (IEEE Trans. Autom. Control., 2020)

Gradient-Based Simulation Optimization for Economic Design of Control Charts. - Gongbo Zhang; Yijie Peng; Shuhuai Yang (CASE, 2021)

Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation. - Yijie Peng; Michael C. Fu 001; Jiaqiao Hu; Pierre L'Ecuyer; Bruno Tuffin (WSC, 2021)

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

Noise Optimization for Artificial Neural Networks. - Li Xiao 005; Zeliang Zhang; Yijie Peng (CoRR, 2021)

Pathological Image Segmentation with Noisy Labels. - Li Xiao 005; Yinhao Li; Luxi Qv; Xinxia Tian; Yijie Peng; S. Kevin Zhou (CoRR, 2021)

Computing Sensitivities for Distortion Risk Measures. - Peter W. Glynn; Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu (INFORMS J. Comput., 2021)

Efficient Sampling Allocation Procedures for Optimal Quantile Selection. - Yijie Peng; Chun-Hung Chen; Michael C. Fu 001; Jian-Qiang Hu; Ilya O. Ryzhov (INFORMS J. Comput., 2021)

Efficient Learning for Selecting Important Nodes in Random Network. - Haidong Li; Xiaoyun Xu; Yijie Peng; Chun-Hung Chen (IEEE Trans. Autom. Control., 2021)

Noise Optimization in Artificial Neural Networks. - Li Xiao 005; Zeliang Zhang; Jinyang Jiang 001; Yijie Peng (CASE, 2022)

Quantile-Based Policy Optimization for Reinforcement Learning. - Jinyang Jiang 001; Yijie Peng; Jiaqiao Hu (WSC, 2022)

Estimating Confidence Regions for Distortion Risk Measures and Their Gradients. - Lei Lei 005; Christos Alexopoulos; Yijie Peng; James R. Wilson (WSC, 2022)

Thompson Sampling Meets Ranking and Selection. - Yijie Peng; Gongbo Zhang (WSC, 2022)

An Efficient Dynamic Sampling Policy for Monte Carlo Tree Search. - Gongbo Zhang; Yijie Peng; Yilong Xu (WSC, 2022)

Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange. - Haidong Li; Anzhi Sheng; Yijie Peng; Long Wang 001 (CoRR, 2022)

A Stochastic Approximation Method for Simulation-Based Quantile Optimization. - Jiaqiao Hu; Yijie Peng; Gongbo Zhang; Qi Zhang (INFORMS J. Comput., 2022)

A New Likelihood Ratio Method for Training Artificial Neural Networks. - Yijie Peng; Li Xiao 005; Bernd Heidergott; L. Jeff Hong; Henry Lam (INFORMS J. Comput., 2022)

Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination. - Zhongshun Shi; Yijie Peng; Leyuan Shi; Chun-Hung Chen; Michael C. Fu 001 (INFORMS J. Comput., 2022)

Efficient Bandwidth Selection for Kernel Density Estimation. - Haidong Li; Long Wang 001; Yijie Peng; Di Wang (WSC, 2023)

Top-Two Thompson Sampling for Selecting Context-Dependent Best Designs. - Xinbo Shi; Yijie Peng; Gongbo Zhang (WSC, 2023)

A Simulation Optimization Method for Scheduling Automated Guided Vehicles in a Stochastic Warehouse Management System. - Gongbo Zhang; Xiaotian Liu; Yijie Peng (WSC, 2023)

POMDP-Based Ranking and Selection. - Ruihan Zhou; Yijie Peng (WSC, 2023)

A Novel Noise Injection-based Training Scheme for Better Model Robustness. - Zeliang Zhang; Jinyang Jiang 001; Minjie Chen; Zhiyuan Wang; Yijie Peng; Zhaofei Yu (CoRR, 2023)

Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms. - Jinyang Jiang 001; Jiaqiao Hu; Yijie Peng (CoRR, 2023)

Training Neural Networks without Backpropagation: A Deeper Dive into the Likelihood Ratio Method. - Jinyang Jiang 001; Zeliang Zhang; Chenliang Xu; Zhaofei Yu; Yijie Peng (CoRR, 2023)

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

Copula sensitivity analysis for portfolio credit derivatives. - Lei Lei 005; Yijie Peng; Michael C. Fu 001; Jian-Qiang Hu (Eur. J. Oper. Res., 2023)

Efficient Sampling Policy for Selecting a Subset With the Best. - Gongbo Zhang; Bin Chen 003; Qing-Shan Jia; Yijie Peng (IEEE Trans. Autom. Control., 2023)

RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search. - Tao Ren 006; Ruihan Zhou; Jinyang Jiang 001; Jiafeng Liang; Qinghao Wang; Yijie Peng (ICAIF, 2024)

One Forward is Enough for Neural Network Training via Likelihood Ratio Method. - Jinyang Jiang 001; Zeliang Zhang; Chenliang Xu; Zhaofei Yu; Yijie Peng (ICLR, 2024)

Distortion Risk Measure-Based Deep Reinforcement Learning. - Jinyang Jiang 001; Bernd Heidergott; Jiaqiao Hu; Yijie Peng (WSC, 2024)

Finite Budget Allocation Improvement in Ranking and Selection. - Xinbo Shi; Yijie Peng; Bruno Tuffin (WSC, 2024)

Dynamic Assortment Optimization in Live-Streaming Sales. - Zishi Zhang; Haidong Li; Ying Liu; Yijie Peng (WSC, 2024)

Solving Mixed Integer Linear Programs by Monte Carlo Tree Search. - Gongbo Zhang; Yijie Peng (WSC, 2024)

Fixed-Precision Ranking and Selection as Markov Decision Process. - Ruihan Zhou; Yijie Peng (WSC, 2024)

AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems. - Ruihan Zhou; L. Jeff Hong; Yijie Peng (CoRR, 2024)

Sample-Efficient Clustering and Conquer Procedures for Parallel Large-Scale Ranking and Selection. - Zishi Zhang; Yijie Peng (CoRR, 2024)

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode. - Jinyang Jiang 001; Xiaotian Liu; Tao Ren 006; Qinghao Wang; Yi Zheng; Yufu Du; Yijie Peng; Cheng Zhang (CoRR, 2024)

Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training. - Zeliang Zhang; Jinyang Jiang 001; Zhuo Liu; Susan Liang; Yijie Peng; Chenliang Xu (CoRR, 2024)

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment. - Yi Zheng; Zehao Li; Peng Jiang; Yijie Peng (CoRR, 2024)

Series Expansion of Probability of Correct Selection for Improved Finite Budget Allocation in Ranking and Selection. - Xinbo Shi; Yijie Peng; Bruno Tuffin (CoRR, 2024)

Eliminating Ratio Bias for Gradient-based Simulated Parameter Estimation. - Zehao Li; Yijie Peng (CoRR, 2024)

Efficient Learning for Clustering and Optimizing Context-Dependent Designs. - Haidong Li; Henry Lam; Yijie Peng (Oper. Res., 2024)

A Q-learning algorithm for Markov decision processes with continuous state spaces. - Jiaqiao Hu; Xiangyu Yang; Jian-Qiang Hu; Yijie Peng (Syst. Control. Lett., 2024)

FLOPS: Forward Learning with OPtimal Sampling. - Tao Ren 006; Zishi Zhang; Jinyang Jiang 001; Guanghao Li; Zeliang Zhang; Mingqian Feng; Yijie Peng (ICLR, 2025)

Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs. - Yijie Peng; Michael C. Fu 001; Jiaqiao Hu; Pierre L'Ecuyer; Bruno Tuffin (Eur. J. Oper. Res., 2025)

An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks: INFORMS Journal on Computing Meritorious Paper Awardee. - Xiaotian Liu; Yijie Peng; Gongbo Zhang; Ruihan Zhou (INFORMS J. Comput., 2025)

Noise Optimization in Artificial Neural Networks. - Li Xiao 005; Zeliang Zhang; Kuihua Huang; Jinyang Jiang 001; Yijie Peng (IEEE Trans Autom. Sci. Eng., 2025)

Efficient Learning for Selecting Top-m Context-Dependent Designs. - Gongbo Zhang; Sihua Chen; Kuihua Huang; Yijie Peng (IEEE Trans Autom. Sci. Eng., 2025)

SIRE-Net: Structure-Integrated Refinement and Enhancement Network for Limited-Angle CT Reconstruction. - Xin Tian; Yijie Peng; Hong Zhang; Dalong Tan; Yixin He; Min Yang (IEEE Trans. Instrum. Meas., 2025)

Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer. - Tao Ren 006; Zishi Zhang; Zehao Li; Jingyang Jiang; Shentao Qin; Guanghao Li; Yan Li; Yi Zheng; Xinping Li; Min Zhan; Yijie Peng (CoRR, 2025)

CoNNect: A Swiss-Army-Knife Regularizer for Pruning of Neural Networks. - Christian Franssen; Jinyang Jiang 001; Yijie Peng; Bernd Heidergott (CoRR, 2025)

A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation. - Zehao Li; Yijie Peng (CoRR, 2025)

Forward Learning with Differential Privacy. - Mingqian Feng; Zeliang Zhang; Jinyang Jiang 001; Yijie Peng; Chenliang Xu (CoRR, 2025)