Navigating the Challenges of Selective Classification Under Differential Privacy: An Empirical Study

In machine learning, differential privacy (DP) and selective classification (SC) are essential for safeguarding sensitive data. DP adds noise to preserve individual privacy while maintaining data utility, while SC improves reliability by allowing models to abstain from predictions when uncertain. This intersection is vital in ensuring model accuracy and reliability in privacy-sensitive applications like healthcare […]

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