Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness
Authors: Xiaoge Deng, Tao Sun, Shengwei Li, Dongsheng Li, Xicheng Lu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate our theoretical findings by training numerous machine learning models, including convex problems and non-convex tasks in computer vision and natural language processing. |
| Researcher Affiliation | Academia | Xiaoge Deng Tao Sun Shengwei Li Dongsheng Li Xicheng Lu College of Computer Science and Technology National University of Defense Technology, China EMAIL, EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | The ASGD procedure is described in Algorithm 1 (located in Appendix A.1). |
| Open Source Code | Yes | We have submitted the source code in the Supplementary Material and provided sufficient instructions for usage in the README.md file. |
| Open Datasets | Yes | For the convex optimization problem, we employed a single-layer linear network with the mean squared error for a classification task on the RCV1 data set from the LIBSVM database [10]. |
| Dataset Splits | No | The paper mentions training data and test datasets but does not explicitly provide information on training/validation/test splits, such as percentages or sample counts for each split. |
| Hardware Specification | Yes | All of our experiments were implemented with PyTorch on Nvidia RTX-3090 24 GB GPUs. |
| Software Dependencies | No | The paper mentions 'PyTorch' as the implementation framework but does not specify its version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | Following our theoretical findings, we set the learning rate to 0.1/τ for different delays, where τ denotes the average delay. |