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2020 – today
- 2024
[c6]Huiqun Li, Hanhan Zhou, Yifei Zou, Dongxiao Yu, Tian Lan:
ConcaveQ: Non-monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning. AAAI 2024: 17461-17468- [c5]Yongsheng Mei, Hanhan Zhou, Tian Lan:
Projection-Optimal Monotonic Value Function Factorization in Multi-Agent Reinforcement Learning. AAMAS 2024: 2381-2383 - [i13]Zuyuan Zhang, Hanhan Zhou, Mahdi Imani, Taeyoung Lee, Tian Lan:
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction. CoRR abs/2403.15341 (2024) - [i12]Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space. CoRR abs/2410.16517 (2024) - 2023
- [j1]Hanhan Zhou
, Tian Lan, Vaneet Aggarwal:
Value Functions Factorization With Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients. IEEE Trans. Emerg. Top. Comput. Intell. 7(5): 1351-1361 (2023) - [c4]Yongsheng Mei, Hanhan Zhou, Tian Lan, Guru Venkataramani, Peng Wei:
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization. AAMAS 2023: 466-475 - [c3]Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding:
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction. NeurIPS 2023 - [i11]Yongsheng Mei, Hanhan Zhou, Tian Lan:
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning. CoRR abs/2302.05593 (2023) - [i10]Yongsheng Mei, Hanhan Zhou, Tian Lan, Guru Venkataramani, Peng Wei:
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization. CoRR abs/2302.10418 (2023) - [i9]Chang-Lin Chen, Hanhan Zhou, Jiayu Chen, Mohammad Pedramfar, Vaneet Aggarwal, Tian Lan, Zheqing Zhu, Chi Zhou, Tim Gasser, Pol Mauri Ruiz, Vijay Menon, Neeraj Kumar, Hongbo Dong:
Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation via Deep Reinforcement Learning. CoRR abs/2306.17054 (2023) - [i8]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning. CoRR abs/2308.14897 (2023) - [i7]Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding:
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction. CoRR abs/2310.08670 (2023) - [i6]Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina C. Adam, Nathaniel D. Bastian, Tian Lan:
Real-time Network Intrusion Detection via Decision Transformers. CoRR abs/2312.07696 (2023) - [i5]Huiqun Li, Hanhan Zhou, Yifei Zou, Dongxiao Yu, Tian Lan:
ConcaveQ: Non-Monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning. CoRR abs/2312.15555 (2023) - 2022
- [c2]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning. NeurIPS 2022 - [i4]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients. CoRR abs/2201.01247 (2022) - [i3]Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding:
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning. CoRR abs/2201.11803 (2022) - [i2]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning. CoRR abs/2206.11420 (2022) - 2021
- [i1]Hanhan Zhou, Tian Lan, Guru Venkataramani:
PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer. CoRR abs/2111.12167 (2021) - 2020
- [c1]Hanhan Zhou, Tian Lan, Guru Venkataramani:
Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration. FEAST@CCS 2020: 17-22
Coauthor Index
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