12 citations · 15 across the 6 of their papers we have counts for
6 papers
LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning
Xueming Yan, Bo Yin, Yaochu Jin
Multiobjective reinforcement learning (MORL) poses significant challenges due to the inherent conflicts between objectives and the difficulty of adapting to dynamic environments. T…
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks
Yaochu Jin, Xueming Yan, Shiqing Liu +1
Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial optimization problems (COPs), exhibiting state-of-the-art performance in both graph-structure…
Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
Xueming Yan, Ziqi Wang, Yaochu Jin
Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. However, current methods face challenges d…
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization
Xueming Yan, Yaochu Jin
Evolutionary algorithms have been successful in solving multi-objective optimization problems (MOPs). However, as a class of population-based search methodology, evolutionary algor…
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
Shangshang Yang, Xiaoshan Yu, Ye Tian +3
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises. Despite the excellent performance of existing Transfor…
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
Shiqing Liu, Xueming Yan, Yaochu Jin
In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform…