Multi-view self-supervised learning for label-flipping robustness in tabular data: a comparative study
Tabular machine learning models are widely used in high-stakes domains such as finance, healthcare, and network intrusion detection, where their reliability depends heavily on accurate training labels. However, malicious or accidental label flipping can corrupt training data and degrade downstream model performance. Re...