Lei Ying
Professor, EECS – Electrical and Computer Engineering
Studies reinforcement learning and stochastic systems, including safe online learning, cloud and communication networks and graph mining.
Lei Ying's lab at Michigan works on reinforcement learning and learning theory & optimization. 29 papers since 2023, 17 at top venues, mostly NeurIPS and ICML. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 59%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 4 papers2026 · T1 1 · collecting
- 5 papers2025 · T1 4
- 9 papers2024 · T1 4
- 11 papers2023 · T1 8
T1 venuesOtherVenues: NeurIPS, ICML, AAAI, AISTATS, COLT
Research focuspaper titles that use each topic's words
- Learning theory & optimization11 papers
- Reinforcement learning9 papers
- communication networks0 papers
- studies reinforcement0 papers
- stochastic0 papers
Paper list
From DBLP.
20264 papers
- COLT (T1)Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds (opens in a new tab)
- Trans. Mach. Learn. Res.Divide and Conquer: Selective Value Learning and Policy Optimization for Offline Safe Reinforcement Learning (opens in a new tab)
- Comput. Commun.From classification to optimization: Slicing and resource management with TRACTOR (opens in a new tab)
- IEEE Trans. Netw.Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems (opens in a new tab)
20255 papers
- NeurIPS (T1)Achieving ~𝒪(1/N) Optimality Gap in Restless Bandits through Gaussian Approximation (opens in a new tab)
- Manuf. Serv. Oper. Manag.An Integrated Approach to Improving Itinerary Completion in Coordinated Care Networks (opens in a new tab)
- WWW (T1)Joint Optimal Transport and Embedding for Network Alignment (opens in a new tab)
- NeurIPS (T1)Near-Optimal Regret-Queue Length Tradeoff in Online Learning for Two-Sided Markets (opens in a new tab)
- ICLR (T1)Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference (opens in a new tab)
20249 papers
- IEEE Trans. Intell. Transp. Syst.A Reinforcement Learning and Prediction-Based Lookahead Policy for Vehicle Repositioning in Online Ride-Hailing Systems (opens in a new tab)
- RLJCost Aware Best Arm Identification (opens in a new tab)
- AAAI (T1)Deep Reinforcement Learning for Early Diagnosis of Lung Cancer (opens in a new tab)
- JMLR (T1)Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment (opens in a new tab)
- ICML (T1)Graph Mixup on Approximate Gromov-Wasserstein Geodesics (opens in a new tab)
- SIGMETRICS Perform. Evaluation Rev.Message from the Technical Program Committee Chairs (opens in a new tab)
- MobiHocOptimistic Joint Flow Control and Link Scheduling with Unknown Utility Functions (opens in a new tab)
- RLJReinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis (opens in a new tab)
- AAAI (T1)Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration (opens in a new tab)
202311 papers
- TKDE (T1)Adversarial Attacks on Multi-Network Mining: Problem Definition and Fast Solutions (opens in a new tab)
- IEEE/ACM Trans. Netw.Age-Dependent Distributed MAC for Ultra-Dense Wireless Networks (opens in a new tab)
- IEEE/ACM Trans. Netw.Efficient Distributed Threshold-Based Offloading for Large-Scale Mobile Cloud Computing (opens in a new tab)
- NeurIPS (T1)Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms (opens in a new tab)
- AISTATS (T1)Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB (opens in a new tab)
- MobiHocNetwork Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach (opens in a new tab)
- ICML (T1)On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures (opens in a new tab)
- ICML (T1)Online Nonstochastic Control with Adversarial and Static Constraints (opens in a new tab)
- AISTATS (T1)Provably Efficient Model-Free Algorithms for Non-stationary CMDPs (opens in a new tab)
- KDD (T1)Reconstructing Graph Diffusion History from a Single Snapshot (opens in a new tab)
- NeurIPS (T1)Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
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- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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