Ji-Ye Liang

Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning

Invariant Subgraph Learning via Effective Environment Subgraph Augmentation for Generalizable Graph Neural Network

With the increasing prevalence of graph data in various practical applications, Graph Neural Networks (GNNs) have established themselves as essential tools for effective graph data processing. However, existing GNNs always perform well on in-distribution data, but exhibit significant performance degradation under distr...

FANS: A Flatness-Aware Network Structure for Generalization in Offline Reinforcement Learning

FANS is presented, a generalization-oriented structured network framework that promotes flatter and robust policy learning by guiding the optimization trajectory through modular architectural design and validate the effectiveness of FANS in mitigating overestimation and promoting generalization.

HyperMixup: Hypergraph-Augmented with Higher-order Information Mixup

The proposed framework advances hypergraph representation learning by unifying data augmentation with higher-order topological constraints, offering both practical utility and theoretical insights for relational machine learning.