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cs.LG2026
Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition
Eeshaan Jain, Linus Bleistein, Bart Deplancke +1
Recent progress in multimodal, high-dimensional learning has enabled foundation models to process heterogeneous, large-scale data. However, at test time, acquiring all features or…
cs.LG2026
Causal Evaluation of Membership Inference Attacks
Mathieu Even, Clément Berenfeld, Linus Bleistein +3
Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy ris…