Cover Image

Adaptive Sampling and Clipping for Private Worst-Case Group Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cairney-Leeming, Max, Sanyal, Amartya, Lampert, Christoph H.
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

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
by: Düngler, Johanna, et al.
Published: (2025)

Demystifying amortized causal discovery with transformers
by: Montagna, Francesco, et al.
Published: (2024)

Provable Privacy with Non-Private Pre-Processing
by: Hu, Yaxi, et al.
Published: (2024)

On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
by: Dmitriev, Daniil, et al.
Published: (2024)

Differentially Private Steering for Large Language Model Alignment
by: Goel, Anmol, et al.
Published: (2025)

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

Less Noise, Same Certificate: Retain Sensitivity for Unlearning
by: Heinzler, Carolin, et al.
Published: (2026)

Language Generation with Replay: A Learning-Theoretic View of Model Collapse
by: Racca, Giorgio, et al.
Published: (2026)

Online Learning and Unlearning
by: Hu, Yaxi, et al.
Published: (2025)

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
by: Pelikan, Martin, et al.
Published: (2023)

Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes
by: Zakerinia, Hossein, et al.
Published: (2025)

SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Training
by: Soleymani, Dorsa, et al.
Published: (2025)

Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)

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

LoRA and Privacy: When Random Projections Help (and When They Don't)
by: Hu, Yaxi, et al.
Published: (2026)

Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024)

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

Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits
by: Huang, Zhiming, et al.
Published: (2026)

Protecting against simultaneous data poisoning attacks
by: Alex, Neel, et al.
Published: (2024)

Learning in an Echo Chamber: Online Learning with Replay Adversary
by: Dmitriev, Daniil, et al.
Published: (2025)

DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)

DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2026)

Provable unlearning in topic modeling and downstream tasks
by: Wei, Stanley, et al.
Published: (2024)

Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)

Differentially Private Worst-group Risk Minimization
by: Zhou, Xinyu, et al.
Published: (2024)

AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training
by: Li, Zhiyuan, et al.
Published: (2026)

Multitask Learning Can Improve Worst-Group Outcomes
by: Kulkarni, Atharva, et al.
Published: (2023)

Fairness for the People, by the People: Minority Collective Action
by: Ben-Dov, Omri, et al.
Published: (2025)

The Role of Learning Algorithms in Collective Action
by: Ben-Dov, Omri, et al.
Published: (2024)

Adaptive Gradient Clipping for Robust Federated Learning
by: Allouah, Youssef, et al.
Published: (2024)

Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
by: Kalinin, Nikita P., et al.
Published: (2026)

Towards Robust Scaling Laws for Optimizers
by: Volkova, Alexandra, et al.
Published: (2026)

Explainable Clustering Beyond Worst-Case Guarantees
by: Fleissner, Maximilian, et al.
Published: (2024)

AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
by: Zhang, Huiqi, et al.
Published: (2025)

Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
by: Zhao, Linzh, et al.
Published: (2025)

To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
by: Marshall, Noah, et al.
Published: (2024)

Robust Mixture Learning when Outliers Overwhelm Small Groups
by: Dmitriev, Daniil, et al.
Published: (2024)

From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD
by: Lampert, Christoph H., et al.
Published: (2026)

1-Lipschitz Neural Networks are more expressive with N-Activations
by: Prach, Bernd, et al.
Published: (2023)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
by: Kalinin, Nikita P., et al.
Published: (2025)