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..
Communication-Efficient Distributed SVD via Local Power Iterations
Authors: Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to demonstrate the effectiveness of Local Power. |
| Researcher Affiliation | Academia | 1School of Mathematical Sciences, Peking University, China 2Department of Computer Science, Stevens Institute of Technology, USA. |
| Pseudocode | Yes | Algorithm 1 Local Power |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We use 15 datasets available on the LIBSVM website.4 This page contains them all. https://www.csie.ntu. edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | The paper states that data samples are 'randomly shuffled and then partitioned among m nodes' but does not specify explicit train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | All the algorithms start from the same initialization Y0. We fix the target rank to k = 5. We set m = max( n 1000 , 3) so that each node has s = 1, 000 samples, unless n is too small. For three variants of Local Power we fix p = 4 (without decaying p). |