Author

Ming-Jie Hu

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Preprint Sep 2026

The Implementation Cost of Fairness in Service Policy Selection

Service organizations use simulation, pilot studies, and historical data to select service policies that balance aggregate performance and fairness. Existing work primarily evaluates the operational cost of fairness, defined as the performance loss caused by restricting the feasible policy set. In this research, we ide...

Jun-Jie Liu, Ming-Jie Hu, K. Hu et al. · 0 citations

Preprint Oct 2026

Rate-Optimal Quantum Discrete Simulation Optimization

Quantum computing has developed rapidly in recent years and has shown potential advantages over classical computing in various domains. In this paper, we study whether quantum computing can accelerate the convergence rate of discrete simulation optimization in the fixed-budget setting. We first formulate the quantum di...

Ming-Jie Hu, Jian-Qiang Hu · 0 citations

#machine learning May 2026

Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective

A unified large deviations framework for data acquisition in infinite-horizon reinforcement learning is developed and a tractable convex relaxation with explicit constraints is proposed, which proves that the resulting reinforcement learning algorithm is near-robustly optimal under the optimality criterion.

Ming-Jie Hu, Jian-Qiang Hu, En-Lu Zhou · arXiv.org · 0 citations