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71 papers · 1 filter
From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models
Muhammad Ashad Kabir, Sirajam Munira
Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screening methods remain difficult t…
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
Michael Zuo, Inwon Kang, Stacy Patterson +1
We explore the privacy-utility tradeoff of synthetic data generation schemes on tabular financial datasets, a domain characterized by high regulatory risk and severe class imbalanc…
Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
Muhammad Ashad Kabir, Sirajam Munira, Dewan Tasnia Azad +3
Early detection of chronic kidney disease (CKD) is essential for preventing progression to end-stage renal disease. However, existing screening tools - primarily developed using po…
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
Zhiqi Wang, Chengyu Zhang, Yuetian Chen +3
Membership inference attacks (MIAs) pose a significant threat to the privacy of machine learning models and are widely used as tools for privacy assessment, auditing, and machine u…