:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Takagi, Shun, Liew, Seng Pei
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2604.28032
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Analysis of Shuffling Beyond Pure Local Differential Privacy
by: Takagi, Shun, et al.
Published: (2026)

Accelerating Differentially Private Federated Learning via Adaptive Extrapolation
by: Takakura, Shokichi, et al.
Published: (2025)

Differentially Private Sampling from Distributions via Wasserstein Projection
by: Takakura, Shokichi, et al.
Published: (2026)

Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
by: Asi, Hilal, et al.
Published: (2024)

Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond
by: Takakura, Shokichi, et al.
Published: (2025)

Reusing Overtrained Language Models Saturates Scaling
by: Liew, Seng Pei, et al.
Published: (2025)

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
by: Brown, Gavin, et al.
Published: (2021)

FedDuA: Doubly Adaptive Federated Learning
by: Takakura, Shokichi, et al.
Published: (2025)

Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
by: Takagi, Shun, et al.
Published: (2025)

Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints
by: Liew, Seng Pei, et al.
Published: (2026)

Scaling Laws for Upcycling Mixture-of-Experts Language Models
by: Liew, Seng Pei, et al.
Published: (2025)

Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
by: Wang, Shaowei, et al.
Published: (2024)

PLAN: Variance-Aware Private Mean Estimation
by: Aumüller, Martin, et al.
Published: (2023)

DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule
by: Matsumoto, Tomoya, et al.
Published: (2026)

HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
by: Takagi, Shun, et al.
Published: (2024)

Improving the Convergence of Private Shuffled Gradient Methods with Public Data
by: Jiang, Shuli, et al.
Published: (2025)

Dimension-free Private Mean Estimation for Anisotropic Distributions
by: Dagan, Yuval, et al.
Published: (2024)

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)

Tukey Depth Mechanisms for Practical Private Mean Estimation
by: Brown, Gavin, et al.
Published: (2025)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
by: Zagardo, David
Published: (2024)

Differentially-Private Collaborative Online Personalized Mean Estimation
by: Yakimenka, Yauhen, et al.
Published: (2024)

Optimal Survey Design for Private Mean Estimation
by: Chen, Yu-Wei, et al.
Published: (2025)

ResBit: Residual Bit Vector for Categorical Values
by: Fuchi, Masane, et al.
Published: (2023)

Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)

Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning
by: Lee, Kichang, et al.
Published: (2024)

Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
by: Vithana, Sajani, et al.
Published: (2024)

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
by: Brown, Gavin, et al.
Published: (2023)

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

Communication-Constrained Private Decentralized Online Personalized Mean Estimation
by: Yakimenka, Yauhen, et al.
Published: (2025)

Variance-Aware Estimation of Kernel Mean Embedding
by: Wolfer, Geoffrey, et al.
Published: (2022)

Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
by: Nguyen, Lam M., et al.
Published: (2026)

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
by: Tran-Dinh, Quoc, et al.
Published: (2024)

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

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
by: Sabater, César, et al.
Published: (2025)

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting
by: Bakhshaliyev, Jafar, et al.
Published: (2026)

FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
by: Dm, Duc, et al.
Published: (2026)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning
by: Huang, Yihong, et al.
Published: (2025)

ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
by: Kato, Fumiyuki, et al.
Published: (2023)

MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
by: Bakman, Yavuz Faruk, et al.
Published: (2024)