publications

2025

2024

1. NeurIPS 2024

   RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space

   Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Nathaniel D. Bastian, and Tian Lan

   In Advances in Neural Information Processing Systems, 2024

   Bib PDF Website

   @inproceedings{chen2024return,
     author = {Chen, Jingdi and Zhou, Hanhan and Mei, Yongsheng and Joe-Wong, Carlee and Bastian, Nathaniel D. and Lan, Tian},
     booktitle = {Advances in Neural Information Processing Systems},
     title = {RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space},
     volume = {37},
     year = {2024},
   }

2. IEEE-MASS

   Real-time Network Intrusion Detection via Importance Sampled Decision Transformers

   Hanhan Zhou, Jingdi Chen, Yongsheng Mei, Gina Adam, Vaneet Aggarwal, Nathaniel D Bastian, and Tian Lan

   21st IEEE International Conference on Mobile Ad-Hoc and Smart Systems ​(MASS), 2024

   Bib

   @article{chen2023real,
     title = {Real-time Network Intrusion Detection via Importance Sampled Decision Transformers},
     author = {Zhou, Hanhan and Chen, Jingdi and Mei, Yongsheng and Adam, Gina and Aggarwal, Vaneet and Bastian, Nathaniel D and Lan, Tian},
     journal = {21st IEEE International Conference on Mobile Ad-Hoc and Smart Systems ​(MASS)},
     year = {2024}
   }

3. AAMAS 2024

   Projection-Optimal Monotonic Value Function Factorization in Multi-Agent Reinforcement Learning

   Yongsheng Mei, Hanhan Zhou, and Tian Lan

   In Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems, 2024

   Bib

   @inproceedings{mei2024optimal,
     author = {Mei, Yongsheng and Zhou, Hanhan and Lan, Tian},
     title = {Projection-Optimal Monotonic Value Function Factorization in Multi-Agent Reinforcement Learning},
     year = {2024},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
     booktitle = {Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems},
     series = {AAMAS '24}
   }

4. AAAI 2024

   ConcaveQ: Non-Monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning

   Huiqun Li, Hanhan Zhou, Yifei Zou, Dongxiao Yu, and Tian Lan

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

   Bib PDF Website

   @inproceedings{li2024concave,
     title = {ConcaveQ: Non-Monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning},
     volume = {38},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     author = {Li, Huiqun and Zhou, Hanhan and Zou, Yifei and Yu, Dongxiao and Lan, Tian},
     year = {2024},
   }

2023

1. AAAI-AICS

   Real-time network intrusion detection via decision transformers

   Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina Adam, Nathaniel D Bastian, and Tian Lan

   AAAI-24 Workshop on Artificial Intelligence for Cyber Security (AICS), 2023

   Bib

   @article{chen2023ream,
     title = {Real-time network intrusion detection via decision transformers},
     author = {Chen, Jingdi and Zhou, Hanhan and Mei, Yongsheng and Adam, Gina and Bastian, Nathaniel D and Lan, Tian},
     journal = {AAAI-24 Workshop on Artificial Intelligence for Cyber Security (AICS)},
     year = {2023}
   }

2. NeurIPS 2023

   Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

   Hanhan Zhou, Tian Lan, Guru Prasadh Venkataramani, and Wenbo Ding

   In Advances in Neural Information Processing Systems, 2023

   Bib PDF Website

   @inproceedings{zhou2023every,
     author = {Zhou, Hanhan and Lan, Tian and Venkataramani, Guru Prasadh and Ding, Wenbo},
     booktitle = {Advances in Neural Information Processing Systems},
     title = {Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction},
     volume = {36},
     year = {2023},
   }

3. AAMAS 2023

   MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

   Yongsheng Mei, Hanhan Zhou, Tian Lan, Guru Venkataramani, and Peng Wei

   In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

   Bib PDF Website

   @inproceedings{mei2022mac,
     author = {Mei, Yongsheng and Zhou, Hanhan and Lan, Tian and Venkataramani, Guru and Wei, Peng},
     title = {MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization},
     year = {2023},
     isbn = {9781450394321},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
     booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
     pages = {466–475},
     numpages = {10},
     series = {AAMAS '23}
   }

4. IEEE-TETCI

   Value Functions Factorization With Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

   Hanhan Zhou, Tian Lan, and Vaneet Aggarwal

   IEEE Transactions on Emerging Topics in Computational Intelligence, 2023

   Bib PDF Website

   @article{zhou2022value,
     author = {Zhou, Hanhan and Lan, Tian and Aggarwal, Vaneet},
     journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
     title = {Value Functions Factorization With Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients},
     year = {2023},
     volume = {7},
     number = {5},
     pages = {1351-1361},
     publisher = {IEEE},
     doi = {10.1109/TETCI.2023.3293193}
   }

5. FMDM-NeurIPS

   Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning

   Hanhan Zhou, Tian Lan, and Vaneet Aggarwal

   NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023

   Bib PDF Website

   @article{zhou2023statistically,
     title = {Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning},
     author = {Zhou, Hanhan and Lan, Tian and Aggarwal, Vaneet},
     journal = {NeurIPS 2023 Foundation Models for Decision Making Workshop},
     year = {2023}
   }

6. Preprint

   Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation via Deep Reinforcement Learning

   Chang-Lin Chen, Hanhan Zhou, Jiayu Chen, Mohammad Pedramfar, Vaneet Aggarwal, Tian Lan, Zheqing Zhu, Chi Zhou, Tim Gasser, Pol Mauri Ruiz, and 1 more author

   arXiv preprint arXiv:2306.17054, 2023

   Bib PDF

   @article{chen2023two,
     title = {Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation via Deep Reinforcement Learning},
     author = {Chen, Chang-Lin and Zhou, Hanhan and Chen, Jiayu and Pedramfar, Mohammad and Aggarwal, Vaneet and Lan, Tian and Zhu, Zheqing and Zhou, Chi and Gasser, Tim and Ruiz, Pol Mauri and others},
     journal = {arXiv preprint arXiv:2306.17054},
     year = {2023}
   }

7. Preprint

   ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning

   Yongsheng Mei, Hanhan Zhou, and Tian Lan

   arXiv preprint arXiv:2302.05593, 2023

   Bib PDF

   @article{mei2023remix,
     title = {ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning},
     author = {Mei, Yongsheng and Zhou, Hanhan and Lan, Tian},
     journal = {arXiv preprint arXiv:2302.05593},
     year = {2023}
   }

2022

1. NeurIPS 2022

   PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning

   Hanhan Zhou, Tian Lan, and Vaneet Aggarwal

   In Advances in Neural Information Processing Systems, 2022

   Bib PDF Website

   @inproceedings{zhou2022pac,
     author = {Zhou, Hanhan and Lan, Tian and Aggarwal, Vaneet},
     booktitle = {Advances in Neural Information Processing Systems},
     pages = {15757--15769},
     publisher = {Curran Associates, Inc.},
     title = {PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning},
     volume = {35},
     year = {2022}
   }

2. FL-NeurIPS

   Federated Learning with Online Adaptive Heterogeneous Local Models

   Hanhan Zhou, Tian Lan, Guru Prasadh Venkataramani, and Wenbo Ding

   In Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022), 2022

   Bib PDF Website

   @inproceedings{zhou2022federated,
     title = {Federated Learning with Online Adaptive Heterogeneous Local Models},
     author = {Zhou, Hanhan and Lan, Tian and Venkataramani, Guru Prasadh and Ding, Wenbo},
     year = {2022},
     booktitle = {Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)}
   }

2021

1. Preprint

   PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer

   Hanhan Zhou, Tian Lan, and Guru Venkataramani

   arXiv preprint arXiv:2111.12167, 2021

   Bib PDF

   @article{zhou2021pt,
     title = {PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer},
     author = {Zhou, Hanhan and Lan, Tian and Venkataramani, Guru},
     journal = {arXiv preprint arXiv:2111.12167},
     year = {2021},
   }

2020

1. CCS-FEAST

   Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration

   Hanhan Zhou, Tian Lan, and Guru Venkataramani

   In Proceedings of the 2020 ACM Workshop on Forming an Ecosystem Around Software Transformation, 2020

   Bib PDF Website

   @inproceedings{zhou2020hunting,
     author = {Zhou, Hanhan and Lan, Tian and Venkataramani, Guru},
     title = {Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration},
     year = {2020},
     isbn = {9781450380898},
     publisher = {Association for Computing Machinery},
     doi = {10.1145/3411502.3418426},
     booktitle = {Proceedings of the 2020 ACM Workshop on Forming an Ecosystem Around Software Transformation},
     pages = {17–22},
     numpages = {6},
     series = {FEAST'20}
   }

2019