1 citations · 1 across the 5 of their papers we have counts for
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Architecture Alignment With Sparse Priors in Tabular Foundation Models
Tianqi Zhao, Tianyi Zhuang, Shuo Duan +3
Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without task-specific training or ext…
TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering
Tianqi Zhao, Guanyang Wang, Yan Shuo Tan +1
Prior-data Fitted Networks (PFNs) have reframed supervised tabular learning as single-pass in-context inference without per-dataset optimization. Extending this paradigm to cluster…
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…