43 citations · 108 across the 22 of their papers we have counts for
13 papers · 1 filter
Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
Wei Duan, Jie Lu, En Yu +1
Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and communication links as edges. While r…
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
Mengjing Wu, Junyu Xuan, Jie Lu
Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and patholo…
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
Zihe Liu, Jie Lu, Guangquan Zhang +1
Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. Wh…
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Wei Duan, Jie Lu, Junyu Xuan
Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for lear…
Layer-diverse Negative Sampling for Graph Neural Networks
Wei Duan, Jie Lu, Yu Guang Wang +1
Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditiona…
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Wei Duan, Jie Lu, Junyu Xuan
Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph l…
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs general…
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph Convolutional Neural Networks (GCNs) has been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think…
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
Junyu Xuan, Jie Lu, Guangquan Zhang
Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great…
Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model
JaeYoon Kim, Junyu Xuan, Christy Liang +1
High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures. Hierar…
Path Integral Based Convolution and Pooling for Graph Neural Networks
Zheng Ma, Junyu Xuan, Yu Guang Wang +2
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and poolin…
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Zhen Fang, Jie Lu, Feng Liu +2
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- t…
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
Junyu Xuan, Jie Lu, Guangquan Zhang +1
The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and mult…