19 citations · 66 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…