19 citations · 66 across the 13 of their papers we have counts for
13 papers
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
Jingdi Chen, Hanhan Zhou, Yongsheng Mei +4
Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world appl…
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
Zuyuan Zhang, Hanhan Zhou, Mahdi Imani +2
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known…
ConcaveQ: Non-Monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning
Huiqun Li, Hanhan Zhou, Yifei Zou +2
Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-…
Real-time Network Intrusion Detection via Decision Transformers
Jingdi Chen, Hanhan Zhou, Yongsheng Mei +3
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detectio…
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due…
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment. Rece…
Learning-based Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation
Chang-Lin Chen, Hanhan Zhou, Jiayu Chen +8
Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., fe…
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
Yongsheng Mei, Hanhan Zhou, Tian Lan +2
Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay signific…
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning
Yongsheng Mei, Hanhan Zhou, Tian Lan
Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution para…
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value…
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
One of the biggest challenges in Federated Learning (FL) is that client devices often have drastically different computation and communication resources for local updates. To this…
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Value function factorization via centralized training and decentralized execution is promising for solving cooperative multi-agent reinforcement tasks. One of the approaches in thi…
PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer
Hanhan Zhou, Tian Lan, Guru Venkataramani
The virtual try-on system has gained great attention due to its potential to give customers a realistic, personalized product presentation in virtualized settings. In this paper, w…