no code implementations • 5 Jan 2024 • Chuyun Shen, Wenhao Li, Haoqing Chen, Xiaoling Wang, Fengping Zhu, Yuxin Li, Xiangfeng Wang, Bo Jin
CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering.
1 code implementation • 6 Dec 2023 • Junjie Sheng, Zixiao Huang, Chuyun Shen, Wenhao Li, Yun Hua, Bo Jin, Hongyuan Zha, Xiangfeng Wang
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks?
no code implementations • 15 Jun 2023 • Chuyun Shen, Wenhao Li, Ya zhang, Xiangfeng Wang
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation.
no code implementations • 8 Jun 2023 • Junjie Sheng, Wenhao Li, Bo Jin, Hongyuan Zha, Jun Wang, Xiangfeng Wang
Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods.
no code implementations • 18 May 2023 • Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Bo Jin, Hongyuan Zha
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved.
no code implementations • 24 Mar 2023 • Lan Jiang, Ye Mao, Xi Chen, Xiangfeng Wang, Chao Li
Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions.
no code implementations • 19 Mar 2023 • Chaofan Ma, Qisen Xu, Xiangfeng Wang, Bo Jin, Xiaoyun Zhang, Yanfeng Wang, Ya zhang
Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints.
no code implementations • 31 Jan 2023 • Wenhao Li, Xiangfeng Wang, Bo Jin, Jingyi Lu, Hongyuan Zha
Social dilemmas can be considered situations where individual rationality leads to collective irrationality.
no code implementations • 28 Jan 2023 • Xiangfeng Wang, Hongteng Xu, Moyi Yang
Privacy-preserving distributed distribution comparison measures the distance between the distributions whose data are scattered across different agents in a distributed system and cannot be shared among the agents.
no code implementations • 29 Nov 2022 • Haochuan Cui, Junjie Sheng, Bo Jin, Yiqiu Hu, Li Su, Lei Zhu, Wenli Zhou, Xiangfeng Wang
With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences.
no code implementations • 21 Nov 2022 • Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 9 Feb 2022 • Moyi Yang, Junjie Sheng, Xiangfeng Wang, Wenyan Liu, Bo Jin, Jun Wang, Hongyuan Zha
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.
1 code implementation • 8 Feb 2022 • Wenhao Li, Hongjun Chen, Bo Jin, Wenzhe Tan, Hongyuan Zha, Xiangfeng Wang
The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.
Multi-Agent Path Finding Multi-agent Reinforcement Learning +1
2 code implementations • 9 Dec 2021 • Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hongyuan Zha, Xiangfeng Wang
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
no code implementations • 15 Nov 2021 • Wenhao Li, Qisen Xu, Chuyun Shen, Bin Hu, Fengping Zhu, Yuxin Li, Bo Jin, Xiangfeng Wang
Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data.
no code implementations • ICLR 2022 • Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Hongyuan Zha
In this paper, we introduce a novel notion, the $\delta$-measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies.
no code implementations • 9 Feb 2021 • Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Yun Hua, Hongyuan Zha
In order to improve the efficiency of cooperation and exploration, we propose a structured diversification emergence MARL framework named {\sc{Rochico}} based on reinforced organization control and hierarchical consensus learning.
no code implementations • 1 Jan 2021 • Wenyan Liu, Xiangfeng Wang, Xingjian Lu, Junhong Cheng, Bo Jin, Xiaoling Wang, Hongyuan Zha
This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.
no code implementations • 28 Jun 2020 • Yunfei Song, Tian Liu, Tongquan Wei, Xiangfeng Wang, Zhe Tao, Mingsong Chen
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 Neural Networks (DNNs) used by Industrial IoT (IIoT) applications.
no code implementations • 17 Apr 2020 • Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity.
Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2
no code implementations • 11 Feb 2020 • Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, Hongyuan Zha
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 11 Feb 2020 • Yun Hua, Xiangfeng Wang, Bo Jin, Wenhao Li, Junchi Yan, Xiaofeng He, Hongyuan Zha
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.
no code implementations • CVPR 2020 • Xuan Liao, Wenhao Li, Qisen Xu, Xiangfeng Wang, Bo Jin, Xiaoyun Zhang, Ya zhang, Yan-Feng Wang
We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL).
no code implementations • 20 Nov 2019 • Jun-Jie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha
To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network.
1 code implementation • 12 Feb 2018 • Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha
However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms.
1 code implementation • 12 Apr 2017 • Wenjie Zhang, Junchi Yan, Xiangfeng Wang, Hongyuan Zha
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
no code implementations • 9 Sep 2015 • Tsung-Hui Chang, Wei-Cheng Liao, Mingyi Hong, Xiangfeng Wang
Unfortunately, a direct synchronous implementation of such algorithm does not scale well with the problem size, as the algorithm speed is limited by the slowest computing nodes.
no code implementations • 9 Sep 2015 • Tsung-Hui Chang, Mingyi Hong, Wei-Cheng Liao, Xiangfeng Wang
By formulating the learning problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology.
no code implementations • 4 Mar 2015 • Changsheng Li, Xiangfeng Wang, Weishan Dong, Junchi Yan, Qingshan Liu, Hongyuan Zha
In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.
no code implementations • 18 Dec 2014 • Changsheng Li, Fan Wei, Weishan Dong, Qingshan Liu, Xiangfeng Wang, Xin Zhang
MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.