3 accepted papers
Semi-supervised graph anomaly detection (GAD), which assumes a subset of annotated normal nodes available during training, is among the most widely explored applications. However, the normality learned by existing semi-supervised GAD methods is limited to the labeled normal nodes, often inclining to…
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this…
This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information. We present SeUL, a novel method that enables selective and fine-grained unlearning for language models…