5 papers
TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering
Tianqi Zhao, Guanyang Wang, Yan Shuo Tan +1
Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data-generating mechanisms, and the absence of transferable inductive b…
Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
Tianqi Zhao, Russa Biswas, Megha Khosla
Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. St…
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
Tianqi Zhao, Megha Khosla
Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superio…
A data-centric approach for assessing progress of Graph Neural Networks
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on t…
AGALE: A Graph-Aware Continual Learning Evaluation Framework
Tianqi Zhao, Alan Hanjalic, Megha Khosla
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly…