:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Xue, Rui
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2511.12434
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
by: Sheng, Guangming, et al.
Published: (2024)

Feature Staleness Aware Incremental Learning for CTR Prediction
by: Wang, Zhikai, et al.
Published: (2025)

Degree of Staleness-Aware Data Updating in Federated Learning
by: Liu, Tao, et al.
Published: (2025)

Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
by: Maboud, Yassaman Ebrahimzadeh, et al.
Published: (2024)

A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks
by: Monath, Nicholas, et al.
Published: (2024)

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024)

Cosine-Gated Adam-Decay: Drop-In Staleness-Aware Outer Optimization for Decoupled DiLoCo
by: Shah, Vatsal, et al.
Published: (2026)

Scaling Law for Quantization-Aware Training
by: Chen, Mengzhao, et al.
Published: (2025)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
by: Waleffe, Roger, et al.
Published: (2025)

RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
by: Niam, Arefin, et al.
Published: (2025)

This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
by: Bardou, Anthony, et al.
Published: (2024)

Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication
by: Jain, Keval, et al.
Published: (2026)

Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
by: Xue, Rui, et al.
Published: (2024)

FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
by: Lu, Chaoyi, et al.
Published: (2026)

FedStale: leveraging stale client updates in federated learning
by: Rodio, Angelo, et al.
Published: (2024)

An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
by: Liu, Shiqing, et al.
Published: (2023)

Distributed Deep Learning using Stochastic Gradient Staleness
by: Pham, Viet Hoang, et al.
Published: (2025)

A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation
by: Li, Xiaocan, et al.
Published: (2025)

Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
by: Askin, Baris, et al.
Published: (2025)

Scale Efficient Training for Large Datasets
by: Zhou, Qing, et al.
Published: (2025)

Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
by: Nimmaturi, Datta, et al.
Published: (2025)

Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
by: Wei, Lanning, et al.
Published: (2024)

E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems
by: Xue, Rui, et al.
Published: (2025)

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
by: Chen, Mengzhao, et al.
Published: (2024)

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection
by: Chen, Pengyu, et al.
Published: (2026)

Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
by: Wilhelm, Patrick, et al.
Published: (2026)

Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Training
by: Wu, Yanru, et al.
Published: (2026)

Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs
by: Zheng, Zhiyuan, et al.
Published: (2025)

Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects
by: Khatoonabadi, SayedHassan, et al.
Published: (2023)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)

Conflict-Aware Adversarial Training
by: Xue, Zhiyu, et al.
Published: (2024)

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
by: Polisetty, Sandeep, et al.
Published: (2023)

Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
by: Jung, Hyunji, et al.
Published: (2026)

Efficient Parallelization Layouts for Large-Scale Distributed Model Training
by: Hagemann, Johannes, et al.
Published: (2023)

Random Walk Diffusion for Efficient Large-Scale Graph Generation
by: Bernecker, Tobias, et al.
Published: (2024)

Understanding Quantization of Optimizer States in LLM Pre-training: Dynamics of State Staleness and Effectiveness of State Resets
by: Topollai, Kristi, et al.
Published: (2026)

Efficient End-to-end Language Model Fine-tuning on Graphs
by: Xue, Rui, et al.
Published: (2023)

Celo: Training Versatile Learned Optimizers on a Compute Diet
by: Moudgil, Abhinav, et al.
Published: (2025)

Adaptive Graph Coarsening for Efficient GNN Training
by: Olshevskyi, Rostyslav, et al.
Published: (2025)

Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
by: Abdallah, Hussein, et al.
Published: (2024)