43 citations · 108 across the 22 of their papers we have counts for
5 papers · 2 filters
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…