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cs.LG2025
Estimating Model-Level Membership Inference Vulnerability Without Reference Models
Euodia Dodd, Nataša Krčo, Igor Shilov +2
Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attacks require training numerous, of…
cs.LG2022★ 6 cited
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Vinitra Swamy, Bahar Radmehr, Natasa Krco +2
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainab…