Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …

Anomalous node detection in a static graph faces significant challenges due to the rarity of
anomalies and the substantial cost of labeling their deviant structure and attribute patterns …

Machine Learning (ML) in low-data settings remains an underappreciated yet crucial
problem. Hence, data augmentation methods to increase the sample size of datasets …

Schema matching--the task of finding matches between attributes across disparate data
sources with different tables and hierarchies--is critical for creating interoperable machine …

Machine learning (ML) has the potential to revolutionize various domains, but its adoption is
often hindered by the disconnect between the needs of domain experts and translating …

Bug triage is the task of assigning newly reported bugs to the proper developers or team for
resolution. This is a critical point in software maintenance as it directly influences the time …

Schema matching--the task of finding matches between attributes across disparate data
sources with different tables and hierarchies--is critical for creating interoperable machine …