mlsanityData to 14 Oct 2026

Stability and Generalization of Differentially Private Minimax Problems

arXiv 2204.04858 ↗First posted 11 Apr 2022cs.LG cs.CRcited by 2

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Abstract (arXiv)

In the field of machine learning, many problems can be formulated as the minimax problem, including reinforcement learning, generative adversarial networks, to just name a few. So the minimax problem has attracted a huge amount of attentions from researchers in recent decades. However, there is relatively little work on studying the privacy of the general minimax paradigm. In this paper, we focus on the privacy of the general minimax setting, combining differential privacy together with minimax optimization paradigm. Besides, via algorithmic stability theory, we theoretically analyze the high probability generalization performance of the differentially private minimax algorithm under the strongly-convex-strongly-concave condition. To the best of our knowledge, this is the first time to analyze the generalization performance of general minimax paradigm, taking differential privacy into account.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 45 of its 99 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

45 of 99 references.

References · 45

  1. 2016Deep Learning with Differential Privacycited by 2,728
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33 more
  1. 2019A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approachcited by 143
  2. 2018Differentially Private Empirical Risk Minimization Revisited: Faster and More Generalcited by 128
  3. 2020Near-Optimal Algorithms for Minimax Optimizationcited by 119
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  6. 2020Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problemscited by 77
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  10. 2020Private Reinforcement Learning with PAC and Regret Guaranteescited by 51
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  12. 2021Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivitycited by 43
  13. 2013One-Pass AUC Optimizationcited by 42
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  17. 2020Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advancescited by 38
  18. 2021Stability and Generalization of Stochastic Gradient Methods for Minimax Problemscited by 36
  19. 2017Robust Optimization for Non-Convex Objectivescited by 35
  20. 2018First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problemscited by 34
  21. 2019On Solving Minimax Optimization Locally: A Follow-the-Ridge Approachcited by 34
  22. 2020Improved Algorithms for Convex-Concave Minimax Optimizationcited by 34
  23. 2015Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimizationcited by 32
  24. 2020Stochastic Hamiltonian Gradient Methods for Smooth Gamescited by 32
  25. 2021Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimizationcited by 31
  26. 2020Generalization Bounds for Stochastic Saddle Point Problemscited by 28
  27. 2022Differentially Private SGDA for Minimax Problemscited by 23
  28. 2021Differentially Private SGD with Non-Smooth Lossescited by 22
  29. 2019Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processescited by 20
  30. 2021Proximal Gradient Descent-Ascent: Variable Convergence under KL Geometrycited by 18
  31. 2019Stochastic Proximal AUC Maximizationcited by 14
  32. 2020Relative Deviation Margin Boundscited by 7
  33. 2020Privacy Enhancing Machine Learning via Removal of Unwanted Dependenciescited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimizationcited by 22
  2. 2024Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problemscited by 7