151 citations · 460 across the 31 of their papers we have counts for
15 papers · 1 filter
Towards Data-centric Graph Machine Learning: Review and Outlook
Xin Zheng, Yixin Liu, Zhifeng Bao +4
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent yea…
Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai, Daochen Zha, Huiyuan Chen +5
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbal…
Efficient GNN Explanation via Learning Removal-based Attribution
Yao Rong, Guanchu Wang, Qizhang Feng +4
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultan…
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Qizhang Feng, Ninghao Liu, Fan Yang +3
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusti…
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Daochen Zha, Louis Feng, Liang Luo +8
Sharding a large machine learning model across multiple devices to balance the costs is important in distributed training. This is challenging because partitioning is NP-hard, and…
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
Shenghan Zhang, Haoxuan Li, Ruixiang Tang +5
Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) pro…