:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jiang, Shuli, Sharma, Pranay, Wu, Zhiwei Steven, Joshi, Gauri
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.03652
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimized Tradeoffs for Private Prediction with Majority Ensembling
by: Jiang, Shuli, et al.
Published: (2024)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

Natural Policy Gradient for Average Reward Non-Stationary RL
by: Jali, Neharika, et al.
Published: (2025)

Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning
by: Khodadadian, Sajad, et al.
Published: (2022)

Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2025)

On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)

Federated Communication-Efficient Multi-Objective Optimization
by: Askin, Baris, et al.
Published: (2024)

FedAST: Federated Asynchronous Simultaneous Training
by: Askin, Baris, et al.
Published: (2024)

Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
by: Armacki, Aleksandar, et al.
Published: (2024)

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

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

Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)

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

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
by: Thaker, Pratiksha, et al.
Published: (2023)

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
by: Nayak, Anupam, et al.
Published: (2026)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
by: Medyakov, Daniil, et al.
Published: (2025)

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

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

Shuffling-Aware Optimization for Private Vector Mean Estimation
by: Takagi, Shun, et al.
Published: (2026)

Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)

ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers
by: Patel, Shivam, et al.
Published: (2025)

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

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

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

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
by: He, Qi, et al.
Published: (2025)

Adaptive Federated Learning via Dynamical System Model
by: Agarwal, Aayushya, et al.
Published: (2025)

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning
by: Jali, Neharika, et al.
Published: (2026)

LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison, and Selection
by: Patel, Shivam, et al.
Published: (2026)

FedECADO: A Dynamical System Model of Federated Learning
by: Agarwal, Aayushya, et al.
Published: (2024)

MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models
by: Raje, Arian, et al.
Published: (2026)

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

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

Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025)

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
by: Maddock, Samuel, et al.
Published: (2025)

Differentially Private Learning Beyond the Classical Dimensionality Regime
by: Dwork, Cynthia, et al.
Published: (2024)

FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)

Erasure Coded Neural Network Inference via Fisher Averaging
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)

Locally Private Sampling with Public Data
by: Zamanlooy, Behnoosh, et al.
Published: (2024)

Distributed Gradient Clustering: Convergence and the Effect of Initialization
by: Armacki, Aleksandar, et al.
Published: (2026)

Differentially Private Verification of Distribution Properties
by: Du, Elbert, et al.
Published: (2026)