Saved in:
| Main Authors: | Chen, Ding, Liu, Chen |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Differentially Private Random Block Coordinate Descent
by: Maranjyan, Artavazd, et al.
Published: (2024)
by: Maranjyan, Artavazd, et al.
Published: (2024)
Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
by: Chien, Eli, et al.
Published: (2025)
by: Chien, Eli, et al.
Published: (2025)
Differentially Private Non-convex Distributionally Robust Optimization
by: Xu, Difei, et al.
Published: (2026)
by: Xu, Difei, et al.
Published: (2026)
Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent
by: Yang, Haoyu, et al.
Published: (2024)
by: Yang, Haoyu, et al.
Published: (2024)
From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression
by: Chen, Ziyan, et al.
Published: (2026)
by: Chen, Ziyan, et al.
Published: (2026)
Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)
by: Xia, Xintao, et al.
Published: (2025)
Exploiting Block Coordinate Descent for Cost-Effective LLM Model Training
by: Liu, Zeyu, et al.
Published: (2025)
by: Liu, Zeyu, et al.
Published: (2025)
Block Coordinate Descent for Neural Networks Provably Finds Global Minima
by: Akiyama, Shunta
Published: (2025)
by: Akiyama, Shunta
Published: (2025)
A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023)
by: Yuan, Ganzhao
Published: (2023)
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)
by: Liu, Junkang, et al.
Published: (2026)
Faster Stochastic Optimization with Arbitrary Delays via Asynchronous Mini-Batching
by: Attia, Amit, et al.
Published: (2024)
by: Attia, Amit, et al.
Published: (2024)
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)
by: Yu, Da, et al.
Published: (2022)
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
by: Tatzel, Lukas, et al.
Published: (2024)
by: Tatzel, Lukas, et al.
Published: (2024)
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
by: Ranaweera, Kanishka, et al.
Published: (2025)
by: Ranaweera, Kanishka, et al.
Published: (2025)
Mini-Batch Kernel $k$-means
by: Jourdan, Ben, et al.
Published: (2024)
by: Jourdan, Ben, et al.
Published: (2024)
Perfect Parallelization in Mini-Batch SGD with Classical Momentum Acceleration
by: Garg, Sachin, et al.
Published: (2026)
by: Garg, Sachin, et al.
Published: (2026)
Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective
by: Liu, Mengfan, et al.
Published: (2026)
by: Liu, Mengfan, et al.
Published: (2026)
Mini-Batch Class Composition Bias in Link Prediction
by: Maguire, Kieran, et al.
Published: (2026)
by: Maguire, Kieran, et al.
Published: (2026)
Memory-Efficient Optimization with Factorized Hamiltonian Descent
by: Nguyen, Son, et al.
Published: (2024)
by: Nguyen, Son, et al.
Published: (2024)
Asynchronous Decentralized SGD under Non-Convexity: A Block-Coordinate Descent Framework
by: Zhou, Yijie, et al.
Published: (2025)
by: Zhou, Yijie, et al.
Published: (2025)
Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
by: Xiong, Wenjun, et al.
Published: (2025)
by: Xiong, Wenjun, et al.
Published: (2025)
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
by: Zhang, Ruijia, et al.
Published: (2025)
by: Zhang, Ruijia, et al.
Published: (2025)
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
by: Räisä, Ossi, et al.
Published: (2024)
by: Räisä, Ossi, et al.
Published: (2024)
Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
by: Xu, Difei, et al.
Published: (2026)
by: Xu, Difei, et al.
Published: (2026)
Differentially-Private Multi-Tier Federated Learning
by: Chen, Evan, et al.
Published: (2024)
by: Chen, Evan, et al.
Published: (2024)
The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
by: Li, Jichu, et al.
Published: (2026)
by: Li, Jichu, et al.
Published: (2026)
Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
by: Sommer, Emanuel, et al.
Published: (2026)
by: Sommer, Emanuel, et al.
Published: (2026)
Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
by: Liang, Haodong, et al.
Published: (2025)
by: Liang, Haodong, et al.
Published: (2025)
Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
by: Zhao, Shen-Yi, et al.
Published: (2020)
by: Zhao, Shen-Yi, et al.
Published: (2020)
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
by: Chen, Hao, et al.
Published: (2021)
by: Chen, Hao, et al.
Published: (2021)
The Hidden Cost of Approximation in Online Mirror Descent
by: Schlisselberg, Ofir, et al.
Published: (2025)
by: Schlisselberg, Ofir, et al.
Published: (2025)
Hierarchical Rectified Flow Matching with Mini-Batch Couplings
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
by: Xu, Difei, et al.
Published: (2025)
by: Xu, Difei, et al.
Published: (2025)
Differentially Private Block-wise Gradient Shuffle for Deep Learning
by: Zagardo, David
Published: (2024)
by: Zagardo, David
Published: (2024)
BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference
by: Wu, Xiaoyou, et al.
Published: (2026)
by: Wu, Xiaoyou, et al.
Published: (2026)
Online Sensitivity Optimization in Differentially Private Learning
by: Galli, Filippo, et al.
Published: (2023)
by: Galli, Filippo, et al.
Published: (2023)
BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacy
by: Liu, Haixia, et al.
Published: (2026)
by: Liu, Haixia, et al.
Published: (2026)
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks
by: Ramesh, Amrutha Varshini, et al.
Published: (2024)
by: Ramesh, Amrutha Varshini, et al.
Published: (2024)
Differentially Private Bilevel Optimization
by: Kornowski, Guy
Published: (2024)
by: Kornowski, Guy
Published: (2024)
Differentially Private Optimization with Sparse Gradients
by: Ghazi, Badih, et al.
Published: (2024)
by: Ghazi, Badih, et al.
Published: (2024)
Similar Items
-
Differentially Private Random Block Coordinate Descent
by: Maranjyan, Artavazd, et al.
Published: (2024) -
Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
by: Chien, Eli, et al.
Published: (2025) -
Differentially Private Non-convex Distributionally Robust Optimization
by: Xu, Difei, et al.
Published: (2026) -
Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent
by: Yang, Haoyu, et al.
Published: (2024) -
From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression
by: Chen, Ziyan, et al.
Published: (2026)