DP-KFC approximates the Fisher Information Matrix for KFAC preconditioning via synthetic noise probes and modality frequency statistics, matching private-data performance without consuming privacy budget or introducing distribution shift.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
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AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
ICA and VEIL enable privacy-preserving supervised ML by producing structurally non-invertible encodings aligned with downstream tasks while maintaining predictive utility.
citing papers explorer
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DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
DP-KFC approximates the Fisher Information Matrix for KFAC preconditioning via synthetic noise probes and modality frequency statistics, matching private-data performance without consuming privacy budget or introducing distribution shift.
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When AI reviews science: Can we trust the referee?
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
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Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning
ICA and VEIL enable privacy-preserving supervised ML by producing structurally non-invertible encodings aligned with downstream tasks while maintaining predictive utility.