Copertina

Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Clipping Bias and Remedies

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Qiang, Yemini, Michal, Wai, Hoi-To
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Optimization and Control Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On the Convergence of DP-SGD with Adaptive Clipping
di: Shulgin, Egor, et al.
Pubblicazione: (2024)

Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification
di: Zhou, Duo, et al.
Pubblicazione: (2025)

Asynchronous and Stochastic Distributed Resource Allocation
di: Li, Qiang, et al.
Pubblicazione: (2025)

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
di: Zhang, Xinwei, et al.
Pubblicazione: (2023)

GeoClip: Geometry-Aware Clipping for Differentially Private SGD
di: Gilani, Atefeh, et al.
Pubblicazione: (2025)

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
di: Armacki, Aleksandar, et al.
Pubblicazione: (2026)

Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level
di: Khah, Saleh Vatan, et al.
Pubblicazione: (2025)

DP-SGD Without Clipping: The Lipschitz Neural Network Way
di: Bethune, Louis, et al.
Pubblicazione: (2023)

The Power of Sampling: Dimension-free Risk Bounds in Private ERM
di: Lee, Yin Tat, et al.
Pubblicazione: (2021)

From Gradient Clipping to Normalization for Heavy Tailed SGD
di: Hübler, Florian, et al.
Pubblicazione: (2024)

Efficient Optimization Algorithms for Linear Adversarial Training
di: RIbeiro, Antônio H., et al.
Pubblicazione: (2024)

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
di: Wei, Chengkun, et al.
Pubblicazione: (2025)

Faster Algorithms for User-Level Private Stochastic Convex Optimization
di: Lowy, Andrew, et al.
Pubblicazione: (2024)

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
di: Lowy, Andrew, et al.
Pubblicazione: (2025)

Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
di: Bassily, Raef, et al.
Pubblicazione: (2024)

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
di: Lowy, Andrew, et al.
Pubblicazione: (2021)

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
di: González, Tomás, et al.
Pubblicazione: (2024)

Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
di: Gao, Changyu, et al.
Pubblicazione: (2024)

Distributed Online Private Learning of Convex Nondecomposable Objectives
di: Cheng, Huqiang, et al.
Pubblicazione: (2022)

Optimal Differentially Private Model Training with Public Data
di: Lowy, Andrew, et al.
Pubblicazione: (2023)

DPZero: Private Fine-Tuning of Language Models without Backpropagation
di: Zhang, Liang, et al.
Pubblicazione: (2023)

Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
di: Şimşekli, Umut, et al.
Pubblicazione: (2024)

Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes
di: Zhou, Duo, et al.
Pubblicazione: (2024)

Differentially Private Bilevel Optimization
di: Kornowski, Guy
Pubblicazione: (2024)

Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
di: Kornowski, Guy, et al.
Pubblicazione: (2024)

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
di: Lowy, Andrew, et al.
Pubblicazione: (2024)

Public-data Assisted Private Stochastic Optimization: Power and Limitations
di: Ullah, Enayat, et al.
Pubblicazione: (2024)

The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
di: Allouah, Youssef, et al.
Pubblicazione: (2024)

Differentially Private Random Block Coordinate Descent
di: Maranjyan, Artavazd, et al.
Pubblicazione: (2024)

Private Zeroth-Order Nonsmooth Nonconvex Optimization
di: Zhang, Qinzi, et al.
Pubblicazione: (2024)

Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
di: Islamov, Rustem, et al.
Pubblicazione: (2026)

Attention-Enhanced Graph Filtering for False Data Injection Attack Detection and Localization
di: Abdulin, Ruslan, et al.
Pubblicazione: (2026)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent
di: Hendrikx, Hadrien, et al.
Pubblicazione: (2023)

Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
di: Menart, Michael, et al.
Pubblicazione: (2023)

Differential Privacy via Distributionally Robust Optimization
di: Selvi, Aras, et al.
Pubblicazione: (2023)

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
di: Ribeiro, Antônio H., et al.
Pubblicazione: (2025)

On the Gradient Complexity of Private Optimization with Private Oracles
di: Menart, Michael, et al.
Pubblicazione: (2025)

Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
di: Lowy, Andrew, et al.
Pubblicazione: (2022)

Differentially Private High Dimensional Bandits
di: Shukla, Apurv
Pubblicazione: (2024)

Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
di: Sun, Tao, et al.
Pubblicazione: (2024)