Unverified50% confidenceFactExact time
The paper proposes the Privacy-Budget Adjoint Mirror Descent (PB-AMD) algorithm, which combines pathwise adjoint gradients with entropy updates to solve for the noise variance schedule.
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10/30/2026
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VerifiedDP-SGD is a mechanism for achieving differential privacy in stochastic gradient descent, implemented by clipping per-sample gradients and then adding Gaussian noise.63% similarUnverifiedThe algorithm is based on the differentially private Follow-the-Regularized-Leader framework, adaptively adjusting the learning rate and noise scale to exploit the concentration of the loss sequence when L* is small in order to reduce the impact of noise.62% similarUnverifiedDifferential privacy was proposed by Cynthia Dwork in 2006, with the core idea of adding random noise to gradients or outputs during data statistics or model training.61% similarUnverifiedUnder a linear-quadratic simplification, the paper derives a square-root allocation law: the noise variance (or the allocation of privacy budget) is inversely proportional to the square root of the number of iterations.61% similarUnverifiedThe researchers propose APSD (Asymmetric Policy Optimization via Self-Distillation), which balances behavior preservation and error correction by constructing a correctness-dependent asymmetric target distribution.60% similar
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