2 citations · 3 across the 15 of their papers we have counts for
6 papers · 1 filter
De-attribute to Forget for LLM Unlearning
Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim +3
The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many…
Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
Lehao Li, Qiang Huang, Yihao Ang +3
Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-depe…
RFOD: Random Forest-based Outlier Detection for Tabular Data
Yihao Ang, Peicheng Yao, Yifan Bao +4
Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalie…
Towards Controllable Time Series Generation
Yifan Bao, Yihao Ang, Qiang Huang +2
Time Series Generation (TSG) has emerged as a pivotal technique in synthesizing data that accurately mirrors real-world time series, becoming indispensable in numerous applications…
From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying
Biao Wu, Qiang Huang, Anthony K. H. Tung
Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on th…
TSGBench: Time Series Generation Benchmark
Yihao Ang, Qiang Huang, Yifan Bao +2
Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides…