A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.
arXiv preprint arXiv:2202.02625 , year=
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Three optimized MPC protocols for privacy-preserving vertical federated learning that support global and global-local updates while reducing computation versus naive full-MPC delegation.
citing papers explorer
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Probing Memorization of Tabular In-Context Learning
A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.
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Secure and Privacy-Preserving Vertical Federated Learning
Three optimized MPC protocols for privacy-preserving vertical federated learning that support global and global-local updates while reducing computation versus naive full-MPC delegation.