:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kamath, Gautam, Pour, Alireza F., Regehr, Matthew, Woodruff, David P.
Format:	Preprint
Published:	2025
Subjects:	Data Structures and Algorithms Machine Learning
Online Access:	https://arxiv.org/abs/2509.16180
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
by: Lebeda, Christian Janos, et al.
Published: (2024)

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
by: Kamath, Gautam, et al.
Published: (2023)

Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)

John Ellipsoids via Lazy Updates
by: Woodruff, David P., et al.
Published: (2025)

Optimal Differentially Private Sampling of Unbounded Gaussians
by: Iverson, Valentio, et al.
Published: (2025)

On Socially Fair Low-Rank Approximation and Column Subset Selection
by: Song, Zhao, et al.
Published: (2024)

Better Bounds for the Distributed Experts Problem
by: Woodruff, David P., et al.
Published: (2026)

Sharper Bounds for $\ell_p$ Sensitivity Sampling
by: Woodruff, David P., et al.
Published: (2023)

Ridge Leverage Score Sampling for $\ell_p$ Subspace Approximation
by: Woodruff, David P., et al.
Published: (2024)

Reweighted Solutions for Weighted Low Rank Approximation
by: Woodruff, David P., et al.
Published: (2024)

Coresets for Multiple $\ell_p$ Regression
by: Woodruff, David P., et al.
Published: (2024)

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)

LevAttention: Time, Space, and Streaming Efficient Algorithm for Heavy Attentions
by: Kannan, Ravindran, et al.
Published: (2024)

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
by: Gu, Xin, et al.
Published: (2023)

Guessing Efficiently for Constrained Subspace Approximation
by: Bhaskara, Aditya, et al.
Published: (2025)

Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
by: Chen, Hongjie, et al.
Published: (2024)

Beyond Worst-Case Dimensionality Reduction for Sparse Vectors
by: Silwal, Sandeep, et al.
Published: (2025)

Fast White-Box Adversarial Streaming Without a Random Oracle
by: Feng, Ying, et al.
Published: (2024)

Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
by: Li, Yi, et al.
Published: (2024)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
by: Aliakbarpour, Maryam, et al.
Published: (2025)

Private Mean Estimation with Person-Level Differential Privacy
by: Agarwal, Sushant, et al.
Published: (2024)

CoinPress: Practical Private Mean and Covariance Estimation
by: Biswas, Sourav, et al.
Published: (2020)

The Broader Landscape of Robustness in Algorithmic Statistics
by: Kamath, Gautam
Published: (2024)

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
by: Axiotis, Kyriakos, et al.
Published: (2024)

The Discrete Gaussian for Differential Privacy
by: Canonne, Clément L., et al.
Published: (2020)

Differentially Private and Scalable Estimation of the Network Principal Component
by: Khayatian, Alireza, et al.
Published: (2025)

Online Learning with Limited Information in the Sliding Window Model
by: Braverman, Vladimir, et al.
Published: (2026)

Learning the Positions in CountSketch
by: Li, Yi, et al.
Published: (2023)

Learning-Augmented Moment Estimation on Time-Decay Models
by: Nagawanshi, Soham, et al.
Published: (2026)

How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024)

Sublinear Time Quantum Sensitivity Sampling
by: Song, Zhao, et al.
Published: (2025)

Distribution Learnability and Robustness
by: Ben-David, Shai, et al.
Published: (2024)

Exact Learning of Weighted Graphs Using Composite Queries
by: Goodrich, Michael T., et al.
Published: (2025)

Query-Efficient Correlation Clustering with Noisy Oracle
by: Kuroki, Yuko, et al.
Published: (2024)

Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts
by: Peng, Pan, et al.
Published: (2025)

Efficient and Private Property Testing via Indistinguishability
by: Dwork, Cynthia, et al.
Published: (2025)

Faster Graph Embeddings via Coarsening
by: Fahrbach, Matthew, et al.
Published: (2020)

On the Structure of Replicable Hypothesis Testers
by: Aamand, Anders, et al.
Published: (2025)

Private Selection with Heterogeneous Sensitivities
by: Antonova, Daniela, et al.
Published: (2025)

Efficient Discrepancy Testing for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2024)