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..
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Authors: Tianyi Chen, Georgios Giannakis, Tao Sun, Wotao Yin
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and real data corroborate a significant communication reduction compared to alternatives. |
| Researcher Affiliation | Academia | University of Minnesota Twin Cities, Minneapolis, MN 55455, USA National University of Defense Technology, Changsha, Hunan 410073, China University of California Los Angeles, Los Angeles, CA 90095, USA |
| Pseudocode | Yes | Algorithm 1 LAG-WK, Algorithm 2 LAG-PS |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The paper states: "Performance is also tested on the real datasets [2]: a) linear regression using Housing, Body fat, Abalone datasets; and, b) logistic regression using Ionosphere, Adult, Derm datasets". It also cites "M. Lichman, UCI machine learning repository, 2013. [Online]. Available: http://archive.ics.uci.edu/ml" as [36], which is a well-known public repository. |
| Dataset Splits | No | The paper mentions "Each dataset is evenly split into three workers" but does not provide specific training, validation, or test dataset split percentages, counts, or methodology. |
| Hardware Specification | Yes | All experiments were performed using MATLAB on an Intel CPU @ 3.4 GHz (32 GB RAM) desktop. |
| Software Dependencies | No | The paper states "All experiments were performed using MATLAB" but does not specify a version number for MATLAB or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Stepsizes for LAG-WK, LAG-PS, and GD are chosen as α = 1/L; to optimize performance and guarantee stability, α = 1/(ML) is used in Cyc-IAG and Num-IAG. For LAG-WK, we choose ξd = ξ = 1/D with D = 10, and for LAG-PS, we choose more aggressive ξd = ξ = 10/D with D = 10. For logistic regression, the regularization parameter is set to λ = 10−3. |