Search Results for author:
Found 41 papers, 10 papers with code
Scalable Reinforcement Learning for Virtual Machine Scheduling
This paper introduces a scalable RL framework, called Cluster Value Decomposition Reinforcement Learning (CVD-RL), to surmount the scalability hurdles inherent in large-scale VMS.
A Survey of Automatic Prompt Engineering: An Optimization Perspective
The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates.
Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
Embodied multi-agent systems (EMAS) have attracted growing attention for their potential to address complex, real-world challenges in areas such as logistics and robotics.
GraphThought: Graph Combinatorial Optimization with Thought Generation
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, especially in text processing and generative tasks.
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc.
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control.
Verbalized Bayesian Persuasion
Information design (ID) explores how a sender influence the optimal behavior of receivers to achieve specific objectives.
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed.
Masked Autoencoders are Parameter-Efficient Federated Continual Learners
On the server side, it reconstructs the uploaded restore information to capture the data distribution across previous tasks and different clients, using these reconstructed images to fine-tune discriminative prompt and classifier parameters tailored for classification, thereby alleviating catastrophic forgetting and non-IID issues on a global scale.
Interactive 3D Medical Image Segmentation with SAM 2
The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images.
In-Context Former: Lightning-fast Compressing Context for Large Language Model
With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus.
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering.
Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
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?
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation.
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
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.
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved.
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions.
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning
Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints.
Learning Roles with Emergent Social Value Orientations
Social dilemmas can be considered situations where individual rationality leads to collective irrationality.
Decentralized Entropic Optimal Transport for Distributed Distribution Comparison
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 be shared directly among the agents.
ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests
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.
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning
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
+2
Obtaining Dyadic Fairness by Optimal Transport
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.
Multi-Agent Path Finding with Prioritized Communication Learning
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
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration
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.
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
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.
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning
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.
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
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.
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
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
+3
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
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.
Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning
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).
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
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.
A Fast Proximal Point Method for Computing Exact Wasserstein Distance
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.
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
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.
Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance
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.
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.
