Longbo Huang
Professor
Works on decision intelligence, reinforcement learning, foundation models, and stochastic network optimization.
Longbo Huang's lab at Tsinghua works on reinforcement learning, learning theory & optimization and large language models. 46 papers since 2023. 8 PhD students and 4 master's students. 15 alumni. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 0%
- PhD students
- 8
- MS students
- 4
- alumni listed
- 15
Papers by yearfrom the lab homepage, 2023–2026
- 13 papers2026 · T1 0 · collecting
- 10 papers2025 · T1 0
- 8 papers2024 · T1 0
- 15 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Learning theory & optimization27 papers
- Reinforcement learning24 papers
- stochastic4 papers
- Large language models2 papers
- network optimization1 paper
Paper list
From the lab homepage.
202613 papers
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyBest-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
- Transactions on Machine Learning Research (TMLR), MayBeyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyBeyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyDiffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies
- Proceedings of International Conference on Quantum Communications, Networking, and Computing (QCNC), April .Effective Entanglement Scheduling in Quantum Networks via Deep Reinforcement Learning
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyFinite-time Convergence Analysis of Actor-Critic with Evolving Reward
- Proceedings of the Fourteenth International Conference on Learning Representations (ICLR), AprilFinite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants
- Proceedings of The International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), MayOM2P: Offline Multi-Agent Mean-Flow Policy
- Proceedings of The Forty- Conference on Uncertainty in Artificial Intelligence (UAI), AugustOn the Sublinear Regret of Continuous K-Max Bandits
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyPowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyReparameterization Flow Policy Optimization
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyReparameterization Proximal Policy Optimization
202510 papers
- Proceedings of ACM Sigmetrics (Sigmetrics), June .Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
- Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), April .Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
- Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), OctoberBeyond Static Populations: Efficient Delay-Constrained Scheduling for Dynamic Users via Deep Reinforcement Learning
- Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), AprilEfficient Online Pruning and Abstraction for Imperfect Information Extensive-Form Games
- Proceedings of Forty- International Conference on Machine Learning (ICML), JulyFinite-Time Analysis of Discrete-Time Stochastic Interpolants
- Transactions on Machine Learning Research (TMLR), MarchMixed Sparsity Training: Achieving 4x FLOP Reduction for Transformer Pretraining
- Proceedings of The International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), MayOffline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
- Proceedings of The International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), MayTackling Sparsity in Designated Driver Dispatch with Multi-Agent Reinforcement Learning
- Proceedings of International Conference on Quantum Communications, Networking, and Computing (QCNC), March .Towards Stability and Utility Maximization in Adversarial Quantum Networks under Bandit Feedback
- Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), April .uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
20248 papers
- Proceedings of the Twelfth International Conference on Learning Representations (ICLR), MayA Quadratic Synchronization Rule for Distributed Deep Learning
- TONMulti-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
- Proceedings of the IEEE International Conference on Mobile Ad Hoc and Smart Systems (MASS), September .Offline Learning-based Multi-User Delay-Constrained Scheduling
- Proceedings of the Forty- International Conference on Machine Learning (ICML), JulyProvable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
- Proceedings of the Twelfth International Conference on Learning Representations (ICLR), MayProvably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
- Proceedings of the Forty- International Conference on Machine Learning (ICML), JulyProvably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
- Proceedings of the Forty- International Conference on Machine Learning (ICML), JulyRL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning
- Proceedings of the Thirty- Conference on Neural Information Processing Systems (NeurIPS), DecemberValue-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
202315 papers
- Acta Scientiarum Naturalium Universitatis Sunyatseni (中山大学学报(自然科学版)), 62(05):38-49, SeptemberA Survey of Multi-modal Learning Theory
- Proceedings of the Fortieth International Conference on Machine Learning (ICML), JulyBanker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
- Proceedings of the Eleventh International Conference on Learning Representations (ICLR), MayCollaborative Pure Exploration in Kernel Bandit
- Proceedings of the Eleventh International Conference on Learning Representations (ICLR), May .Generative Augmented Flow Networks
- Synthesis Lectures on Learning, Networks, and Algorithms, Springer Nature, JuneLearning for Decision and Control in Stochastic Networks
- Proceedings of the Fortieth International Conference on Machine Learning (ICML), JulyMulti-task Representation Learning for Pure Exploration in Linear Bandits
- TCOMNetwork Topology Optimization via Deep Reinforcement Learning
- Transactions on Machine Learning Research (TMLR), JulyOnline Min-max Problems: Nonconvexity and Saddle Point
- Proceedings of the Eleventh International Conference on Learning Representations (ICLR), MayProvably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
- Proceedings of the Thirty- Conference on Neural Information Processing Systems (NeurIPS), DecemberProvably Safe Reinforcement Learning with Step-wise Violation Constraints
- Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), FebruaryRePreM: Representation Pre-training with Masked Model for Reinforcement Learning
- Proceedings of the Eleventh International Conference on Learning Representations (ICLR), May .RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Where alumni went15 listed
Apply to TsinghuaFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- GRE
- See program page
- English
- Per school catalog
- Fee
- RMB 800
Not yet confirmed for this intake.
Similar labsnearest by research summary
- CNSJTU
Ruidan Su
0 T1 - CNZhejiang
Lanfen Lin
- Medical image analysis
- CNZhejiang
Jun Xiao
60 T1 - USUC Berkeley
Benjamin Recht
- Learning theory & optimization
- GBEdinburgh
Christopher Lucas
7 T1 - CHEPFL
Pierre Vandergheynst
- Signal processing & information theory
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Tsinghuacloses