Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Difei, Ding, Meng, Xiang, Zihang, Xu, Jinhui, Wang, Di |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
by: Zhang, Ruijia, et al.
Published: (2025)
by: Zhang, Ruijia, et al.
Published: (2025)
Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
by: Xu, Difei, et al.
Published: (2026)
by: Xu, Difei, et al.
Published: (2026)
Towards User-level Private Reinforcement Learning with Human Feedback
by: Zhang, Jiaming, et al.
Published: (2025)
by: Zhang, Jiaming, et al.
Published: (2025)
Differentially Private Non-convex Distributionally Robust Optimization
by: Xu, Difei, et al.
Published: (2026)
by: Xu, Difei, et al.
Published: (2026)
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)
by: Tian, Xizhi, et al.
Published: (2025)
Nearly Optimal Differentially Private ReLU Regression
by: Ding, Meng, et al.
Published: (2025)
by: Ding, Meng, et al.
Published: (2025)
Beyond Tsybakov: Model Margin Noise and $\mathcal{H}$-Consistency Bounds
by: Mohri, Mehryar, et al.
Published: (2025)
by: Mohri, Mehryar, et al.
Published: (2025)
Revisiting Differentially Private Hyper-parameter Tuning
by: Xiang, Zihang, et al.
Published: (2024)
by: Xiang, Zihang, et al.
Published: (2024)
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
by: Li, Yinan, et al.
Published: (2023)
by: Li, Yinan, et al.
Published: (2023)
Understanding Private Learning From Feature Perspective
by: Ding, Meng, et al.
Published: (2025)
by: Ding, Meng, et al.
Published: (2025)
Understanding Forgetting in Continual Learning with Linear Regression
by: Ding, Meng, et al.
Published: (2024)
by: Ding, Meng, et al.
Published: (2024)
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
by: Lowy, Andrew, et al.
Published: (2022)
by: Lowy, Andrew, et al.
Published: (2022)
Beyond Slater's Condition in Online CMDPs with Stochastic and Adversarial Constraints
by: Stradi, Francesco Emanuele, et al.
Published: (2025)
by: Stradi, Francesco Emanuele, et al.
Published: (2025)
Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective
by: Ding, Meng, et al.
Published: (2026)
by: Ding, Meng, et al.
Published: (2026)
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
by: Zhang, Jiaming, et al.
Published: (2026)
by: Zhang, Jiaming, et al.
Published: (2026)
Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)
by: Ding, Youlong, et al.
Published: (2024)
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
by: Zhu, Zehan, et al.
Published: (2024)
by: Zhu, Zehan, et al.
Published: (2024)
FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)
by: Wang, Liangyu, et al.
Published: (2025)
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
by: Wang, Shaowei, et al.
Published: (2024)
by: Wang, Shaowei, et al.
Published: (2024)
An Optimization Framework for Differentially Private Sparse Fine-Tuning
by: Makni, Mehdi, et al.
Published: (2025)
by: Makni, Mehdi, et al.
Published: (2025)
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)
by: Zhang, Xinwei, et al.
Published: (2024)
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)
by: Zhang, Xinwei, et al.
Published: (2024)
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data
by: Wang, Haoling, et al.
Published: (2026)
by: Wang, Haoling, et al.
Published: (2026)
Second-Order Convergence in Private Stochastic Non-Convex Optimization
by: Tao, Youming, et al.
Published: (2025)
by: Tao, Youming, et al.
Published: (2025)
Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)
by: Xiang, Zihang, et al.
Published: (2023)
Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)
by: Pillutla, Krishna, et al.
Published: (2025)
Noise-Aware Differentially Private Variational Inference
by: Alrawajfeh, Talal, et al.
Published: (2024)
by: Alrawajfeh, Talal, et al.
Published: (2024)
Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
by: Zhu, Tianxi, et al.
Published: (2025)
by: Zhu, Tianxi, et al.
Published: (2025)
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
by: Song, Yujee, et al.
Published: (2024)
by: Song, Yujee, et al.
Published: (2024)
Certificate-Guided Pruning for Stochastic Lipschitz Optimization
by: Shihab, Ibne Farabi, et al.
Published: (2026)
by: Shihab, Ibne Farabi, et al.
Published: (2026)
Differentially Private Conditional Independence Testing
by: Kalemaj, Iden, et al.
Published: (2023)
by: Kalemaj, Iden, et al.
Published: (2023)
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
by: Huang, Tao, et al.
Published: (2026)
by: Huang, Tao, et al.
Published: (2026)
Hidden State Differential Private Mini-Batch Block Coordinate Descent for Multi-convexity Optimization
by: Chen, Ding, et al.
Published: (2024)
by: Chen, Ding, et al.
Published: (2024)
Foundation Inference Models for Ordinary Differential Equations
by: Mauel, Maximilian, et al.
Published: (2026)
by: Mauel, Maximilian, et al.
Published: (2026)
Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)
by: Xia, Xintao, et al.
Published: (2025)
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
Optimal Rates for Pure $\varepsilon$-Differentially Private Stochastic Convex Optimization with Heavy Tails
by: Lowy, Andrew
Published: (2026)
by: Lowy, Andrew
Published: (2026)
Similar Items
-
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
by: Zhang, Ruijia, et al.
Published: (2025) -
Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
by: Xu, Difei, et al.
Published: (2026) -
Towards User-level Private Reinforcement Learning with Human Feedback
by: Zhang, Jiaming, et al.
Published: (2025) -
Differentially Private Non-convex Distributionally Robust Optimization
by: Xu, Difei, et al.
Published: (2026) -
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)