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..
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
Authors: Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that our algorithms run efficiently for a variety of problems on modern hardware. |
| Researcher Affiliation | Academia | Christopher De Sa, Ce Zhang, Kunle Olukotun, and Christopher R e EMAIL, EMAIL, EMAIL, EMAIL Departments of Electrical Engineering and Computer Science Stanford University, Stanford, CA 94309 |
| Pseudocode | No | The paper describes algorithms using equations and text but does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for its methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We analyzed all four datasets reported in Dimm Witted [25] that favored HOGWILD!: Reuters and RCV1, which are text classification datasets; Forest, which arises from remote sensing; and Music, which is a music classification dataset. |
| Dataset Splits | No | The paper mentions analyzing datasets and training loss, but it does not explicitly describe any train/validation/test dataset splits or their sizes. |
| Hardware Specification | Yes | Experiments ran on a machine with two Xeon X650 CPUs, each with six hyperthreaded cores, and 24GB of RAM. |
| Software Dependencies | No | The paper mentions algorithms like SGD, HOGWILD!, and BUCKWILD! but does not provide specific version numbers for any software libraries, frameworks, or dependencies used in the experiments. |
| Experiment Setup | Yes | We ran SGD with step size α = 0.0001; however, results are similar across a range of step sizes. |