HOGWILD!-Gibbs can be PanAccurate
Authors: Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 costis@csail.mit.edu Nishanth Dikkala EECS & CSAIL, MIT nishanthd@csail.mit.edu Siddhartha Jayanti EECS & CSAIL, MIT jayanti@mit.edu |
| 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. |