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Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems

Tomás González et al.

SIAM Journal on Optimization2026https://doi.org/10.1137/24m1697268article
AJG 3
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0.50

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@article{tomás2026,
  title        = {{Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems}},
  author       = {Tomás González et al.},
  journal      = {SIAM Journal on Optimization},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1137/24m1697268},
}

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Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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