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..
HOGWILD!-Gibbs can be PanAccurate
Authors: Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on a multiprocessor machine to empirically illustrate our theoretical findings. |
| Researcher Affiliation | Academia | Constantinos Daskalakis EECS & CSAIL, MIT EMAIL Nishanth Dikkala EECS & CSAIL, MIT EMAIL Siddhartha Jayanti EECS & CSAIL, MIT EMAIL |
| Pseudocode | Yes | Input: Set of variables V , Configuration x0 S|V |, Distribution π initialization; for t = 1 to T do Sample i uniformly from {1, 2, . . . , n}; Sample Xi Prπ [.|X i = x i] and set xi,t = Xi; For all j = i, set xj,t = xj,t 1; end Algorithm 1: Gibbs Sampling |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | No | The paper describes using Curie-Weiss and Grid models and generating samples by running Markov chains, but it does not provide concrete access information (link, DOI, formal citation) for a specific publicly available dataset used for the experiments. |
| Dataset Splits | No | The paper does not describe specific training, validation, or test dataset splits (percentages, sample counts, or predefined splits) for reproducibility. It discusses generating samples and comparing two methods directly. |
| Hardware Specification | Yes | We show the results of experiments run on a machine with four 10-core Intel Xeon E7-4850 CPUs |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | To generate samples, we start at a uniformly random configuration and run Markov chains for T = 10n log2(n) steps to ensure mixing. We chose α = 0.5 to ensure Dobrushin s condition. Four asynchronous processors were used to generate the first plot, while twenty were used for the second. Each red point is the empirical mean of the function f computed over 5000 samples from the HOGWILD! Markov chain corresponding to CW(n, 0.5), and each blue point is the empirical mean produced from 5000 sequential runs of the same chain. |