5 papers
Representation Learning on Out of Distribution in Tabular Data
Achmad Ginanjar, Xue Li, Priyanka Singh +1
The open-world assumption in model development suggests that a model might lack sufficient information to adequately handle data that is entirely distinct or out of distribution (O…
Random Client Selection on Contrastive Federated Learning for Tabular Data
Achmad Ginanjar, Xue Li, Priyanka Singh +1
Vertical Federated Learning (VFL) has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulne…
Continual Contrastive Learning on Tabular Data with Out of Distribution
Achmad Ginanjar, Xue Li, Priyanka Singh +1
Out-of-distribution (OOD) prediction remains a significant challenge in machine learning, particularly for tabular data where traditional methods often fail to generalize beyond th…
Contrastive Federated Learning with Tabular Data Silos
Achmad Ginanjar, Xue Li, Wen Hua +1
Learning from vertical partitioned data silos is challenging due to the segmented nature of data, sample misalignment, and strict privacy concerns. Federated learning has been prop…
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
Khai Phan Tran, Wen Hua, Xue Li
Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution…