76 citations · 233 across the 46 of their papers we have counts for
23 papers · 1 filter
DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
Tongzhou Yu, Mingjia Li, Hong Qian +6
Human creativity has emerged as a critical competency in the era of large language models. Assessing creativity in complex, open-ended environments is a grand challenge in data min…
Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents
Wenhao Li, Xiangfeng Wang, Bo Jin
Diffusion-based planning has achieved strong results in single-agent offline reinforcement learning, yet scaling to many-agent systems remains intractable due to the curse of dimen…
AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
Jiehao Wu, Zixiao Huang, Wenhao Li +3
Optimizing AscendC (Ascend C) operators for Ascend NPUs is difficult for two reasons. First, unlike CUDA, the ecosystem offers few public kernels to learn from. Second, performance…
Differentiable Adaptive Kalman Filtering via Optimal Transport
Yangguang He, Wenhao Li, Minzhe Li +3
Learning-based filtering has demonstrated strong performance in non-linear dynamical systems, particularly when the statistics of noise are unknown. However, in real-world deployme…
Learning Virtual Machine Scheduling in Cloud Computing through Language Agents
JieHao Wu, Ziwei Wang, Junjie Sheng +3
In cloud services, virtual machine (VM) scheduling is a typical Online Dynamic Multidimensional Bin Packing (ODMBP) problem, characterized by large-scale complexity and fluctuating…
Scalable Reinforcement Learning for Virtual Machine Scheduling
Junjie Sheng, Jiehao Wu, Haochuan Cui +6
Recent advancements in reinforcement learning (RL) have shown promise for optimizing virtual machine scheduling (VMS) in small-scale clusters. The utilization of RL to large-scale…
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
Yangguang He, Wenhao Li, Minzhe Li +3
State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been…
GraphThought: Graph Combinatorial Optimization with Thought Generation
Zixiao Huang, Lifeng Guo, Wenhao Li +6
Graph combinatorial optimization (GCO) problems are central to domains like logistics and bioinformatics. While traditional solvers dominate, large language models (LLMs) offer new…
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
Yuchen He, Chuyun Shen, Xiangfeng Wang +1
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forge…
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
Wenhao Li, Dan Qiao, Baoxiang Wang +3
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Autom…
Decentralized Entropic Optimal Transport for Distributed Distribution Comparison
Xiangfeng Wang, Hongteng Xu, Moyi Yang
Distributed distribution comparison aims to measure the distance between the distributions whose data are scattered across different agents in a distributed system and cannot even…
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning
Junjie Sheng, Lu Wang, Fangkai Yang +9
Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not al…
Obtaining Dyadic Fairness by Optimal Transport
Moyi Yang, Junjie Sheng, Xiangfeng Wang +4
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning. In this paper, we focus on o…
VMAgent: Scheduling Simulator for Reinforcement Learning
Junjie Sheng, Shengliang Cai, Haochuan Cui +9
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtua…
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning
Wenhao Li, Xiangfeng Wang, Bo Jin +3
When solving a complex task, humans will spontaneously form teams and to complete different parts of the whole task, respectively. Meanwhile, the cooperation between teammates will…
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
Wenhao Li, Xiangfeng Wang, Bo Jin +2
Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning (MARL). One of the reasons is the policy changes of agents during the learning process. Some…
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
Yunfei Song, Tian Liu, Tongquan Wei +3
Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Ne…
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Wenhao Li, Bo Jin, Xiangfeng Wang +2
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse o…
Learning Structured Communication for Multi-agent Reinforcement Learning
Junjie Sheng, Xiangfeng Wang, Bo Jin +5
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology fo…
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
Yun Hua, Xiangfeng Wang, Bo Jin +4
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. In thi…
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
Junjie Wang, Xiangfeng Wang, Bo Jin +3
Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfe…
Deep Extreme Multi-label Learning
Wenjie Zhang, Junchi Yan, Xiangfeng Wang +1
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space w…
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
Changsheng Li, Xiangfeng Wang, Weishan Dong +3
This paper presents an unsupervised learning approach for simultaneous sample and feature selection, which is in contrast to existing works which mainly tackle these two problems s…