5 papers
Relatron: Automating Relational Machine Learning over Relational Databases
Zhikai Chen, Han Xie, Jian Zhang +3
Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. R…
Feedback Control for Multi-Objective Graph Self-Supervision
Karish Grover, Theodore Vasiloudis, Han Xie +3
Can multi-task self-supervised learning on graphs be coordinated without the usual tug-of-war between objectives? Graph self-supervised learning (SSL) offers a growing toolbox of p…
AutoG: Towards automatic graph construction from tabular data
Zhikai Chen, Han Xie, Jian Zhang +4
Recent years have witnessed significant advancements in graph machine learning (GML), with its applications spanning numerous domains. However, the focus of GML has predominantly b…
Dynamic Mixture-of-Experts for Incremental Graph Learning
Lecheng Kong, Theodore Vasiloudis, Seongjun Yun +2
Graph incremental learning is a learning paradigm that aims to adapt trained models to continuously incremented graphs and data over time without the need for retraining on the ful…
Spectro-Riemannian Graph Neural Networks
Karish Grover, Haiyang Yu, Xiang Song +4
Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic…