Cover Image

Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Difei, Tao, Youming, Ding, Meng, Fan, Chenglin, Wang, Di
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Non-convex Distributionally Robust Optimization
by: Xu, Difei, et al.
Published: (2026)

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
by: Xu, Difei, et al.
Published: (2025)

Second-Order Convergence in Private Stochastic Non-Convex Optimization
by: Tao, Youming, et al.
Published: (2025)

Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
by: Zhang, Ruijia, et al.
Published: (2025)

Adaptive Batch Size for Privately Finding Second-Order Stationary Points
by: Liu, Daogao, et al.
Published: (2024)

DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
by: Kim, Jihwan, et al.
Published: (2026)

Noise is All You Need: Private Second-Order Convergence of Noisy SGD
by: Avdiukhin, Dmitrii, et al.
Published: (2024)

The Minimax Rate of Second-Order Calibration
by: Ciosek, Kamil, et al.
Published: (2026)

Towards User-level Private Reinforcement Learning with Human Feedback
by: Zhang, Jiaming, et al.
Published: (2025)

Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
by: Gu, Zhihao, et al.
Published: (2024)

Nearly Optimal Differentially Private ReLU Regression
by: Ding, Meng, et al.
Published: (2025)

Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)

Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
by: Zhou, Qihao, et al.
Published: (2024)

On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2025)

Exactly Minimax-Optimal Locally Differentially Private Sampling
by: Park, Hyun-Young, et al.
Published: (2024)

The Computational Complexity of Finding Stationary Points in Non-Convex Optimization
by: Hollender, Alexandros, et al.
Published: (2023)

Two-timescale Extragradient for Finding Local Minimax Points
by: Chae, Jiseok, et al.
Published: (2023)

Second-Order Forward-Mode Automatic Differentiation for Optimization
by: Cobb, Adam D., et al.
Published: (2024)

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
by: Chien, Eli, et al.
Published: (2025)

Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
by: Zhao, Puning, et al.
Published: (2024)

Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
by: Xia, Xintao, et al.
Published: (2024)

First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
by: Luo, Zhankun, et al.
Published: (2026)

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
by: Yu, Qian, et al.
Published: (2024)

Online AUC Optimization Based on Second-order Surrogate Loss
by: Luo, JunRu, et al.
Published: (2025)

Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
by: Zhang, Jiaming, et al.
Published: (2026)

Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)

Federated Stochastic Minimax Optimization under Heavy-Tailed Noises
by: Zhang, Xinwen, et al.
Published: (2025)

Understanding Private Learning From Feature Perspective
by: Ding, Meng, et al.
Published: (2025)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
by: Jiang, Ruichen, et al.
Published: (2024)

Efficient Stochastic Approximation of Minimax Excess Risk Optimization
by: Zhang, Lijun, et al.
Published: (2023)

Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)

FUSE: First-Order and Second-Order Unified SynthEsis in Stochastic Optimization
by: Jiang, Zhanhong, et al.
Published: (2025)

Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization
by: Chen, Lesi, et al.
Published: (2022)

A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax Problems
by: Wang, Junlin, et al.
Published: (2024)

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
by: Di, Qiwei, et al.
Published: (2024)

Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization
by: Li, Chris Junchi
Published: (2024)

Mitigating Membership Inference in Intermediate Representations with Differentially Private Training
by: Meng, Jiayang, et al.
Published: (2026)

Gradient Norm Regularization Second-Order Algorithms for Solving Nonconvex-Strongly Concave Minimax Problems
by: Wang, Jun-Lin, et al.
Published: (2024)

Stationary MMD Points
by: Chen, Zonghao, et al.
Published: (2025)

PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
by: Zhu, Zehan, et al.
Published: (2024)