I am a third year Ph.D. Candidate in Department of Statistics, The Pennsylvania State University. My research lies at the intersection of Reinforcement Learning (RL) and Federated Learning (FL), with a dual focus on:
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Theoretical Foundations: Designing provably efficient RL/FRL algorithms to achieve optimal learning accuracy while improving computational and communication efficiency.
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Practical Applications: Designing adaptable models that bridge algorithmic innovation with real-world biomedical and healthcare challenges.
You can find my CV here.
I am very furtunate to be advised by Prof. Lingzhou Xue from Department of Statistics, The Pennsylvania State University.
🔥 News
- 2025.05: A paper is accepted by ICML 2025.
- 2025.04: I attended ICLR 2025 in Singapore.
- 2024.11: Two papers are accepted by ICLR 2025.
📝 Publications
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Gap-Dependent Bounds for Federated Q-Learning.
Haochen Zhang, Zhong Zheng, and Lingzhou Xue (2025).
The Forty-second International Conference on Machine Learning (ICML).
Available at arXiv. -
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition.
Zhong Zheng, Haochen Zhang (co-first author), and Lingzhou Xue (2025).
The Thirteenth International Conference on Learning Representations (ICLR).
(Spotlight, 3.26% acceptance rate)
Available at Openreview and arXiv. -
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost.
Zhong Zheng, Haochen Zhang (co-first author), and Lingzhou Xue (2025).
The Thirteenth International Conference on Learning Representations (ICLR).
Available at Openreview and arXiv.
🎖 Honors and Awards
- Gold Medal, 2018 Chinese Mathematical Olympiad (CMO)
- Second Prize, 2020 National Undergraduate Mathematics Competition of China (Class A)
- Second Prize, 2021 National Undergraduate Mathematics Competition of China (Class A)
📖 Educations
Ph.D. in Statistics
The Pennsylvania State University, 2023–Present
Advisor: Dr. Lingzhou Xue
B.Sc. in Statistics
Peking University, 2019–2023