Search Results for author: Xiangfeng Wang

Found 30 papers, 6 papers with code

Complementary Information Mutual Learning for Multimodality Medical Image Segmentation

no code implementations5 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.

Image Segmentation Inductive Bias +4

Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym

1 code implementation6 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?

Benchmarking Decision Making +1

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

no code implementations8 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.

Multi-agent Reinforcement Learning

Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning

no code implementations18 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.

Decision Making Multi-agent Reinforcement Learning +2

CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis

no code implementations24 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.

CoLA Image Generation

Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning

no code implementations19 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.

Image Segmentation Interactive Segmentation +5

Learning Roles with Emergent Social Value Orientations

no code implementations31 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.

Multi-agent Reinforcement Learning Role Embedding

Decentralized Entropic Optimal Transport for Privacy-preserving Distributed Distribution Comparison

no code implementations28 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.

Domain Adaptation Privacy Preserving

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

no code implementations29 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.

Cloud Computing Scheduling

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 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

Obtaining Dyadic Fairness by Optimal Transport

1 code implementation9 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.

Fairness Link Prediction

Multi-Agent Path Finding with Prioritized Communication Learning

1 code implementation8 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

VMAgent: Scheduling Simulator for Reinforcement Learning

2 code implementations9 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.

Cloud Computing reinforcement-learning +2

Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration

no code implementations15 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.

Image Segmentation Interactive Segmentation +4

Dealing with Non-Stationarity in MARL via Trust-Region Decomposition

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.

Multi-agent Reinforcement Learning

Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning

no code implementations9 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.

Multi-agent Reinforcement Learning

Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy

no code implementations1 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.

Fairness

FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

no code implementations28 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.

Federated Learning

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

no code implementations17 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

HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem

no code implementations11 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.

Meta-Learning Meta Reinforcement Learning +2

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning

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).

Image Segmentation Medical Image Segmentation +5

Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

no code implementations20 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.

Generalized Zero-Shot Learning Transfer Learning

A Fast Proximal Point Method for Computing Exact Wasserstein Distance

1 code implementation12 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.

BIG-bench Machine Learning

Deep Extreme Multi-label Learning

1 code implementation12 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.

Classification Extreme Multi-Label Classification +2

Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance

no code implementations9 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.

Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis

no code implementations9 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.

Distributed Optimization

Joint Active Learning with Feature Selection via CUR Matrix Decomposition

no code implementations4 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.

Active Learning feature selection

Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data

no code implementations18 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.

regression

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