DP-Muon adapts matrix-orthogonalized momentum optimization to differential privacy via per-matrix clipping and noise addition, with proofs of inherited privacy and optimization guarantees plus a bias-corrected version that improves private fine-tuning utility.
2014 IEEE 55th Annual Symposium on Foundations of Computer Science , pages=
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Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
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DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
DP-Muon adapts matrix-orthogonalized momentum optimization to differential privacy via per-matrix clipping and noise addition, with proofs of inherited privacy and optimization guarantees plus a bias-corrected version that improves private fine-tuning utility.
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The Threshold Breakdown Point
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.