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Privacy Amplification of Iterative Algorithms via Contraction
  Coefficients

Privacy Amplification of Iterative Algorithms via Contraction Coefficients

17 January 2020
S. Asoodeh
Mario Díaz
Flavio du Pin Calmon
    FedML
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Papers citing "Privacy Amplification of Iterative Algorithms via Contraction Coefficients"

2 / 2 papers shown
Title
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even
  for Non-Convex Losses
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses
S. Asoodeh
Mario Díaz
20
6
0
17 May 2023
Resolving the Mixing Time of the Langevin Algorithm to its Stationary
  Distribution for Log-Concave Sampling
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling
Jason M. Altschuler
Kunal Talwar
38
24
0
16 Oct 2022
1