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 [1].
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Authors: Christopher De Sa, Chris Re, Kunle Olukotun
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that our theoretical results match practical outcomes. and We validate our results experimentally and show that, by using asynchronous execution, we can achieve wall-clock speedups of up to 2.8 on real problems. |
| Researcher Affiliation | Academia | Christopher De Sa EMAIL Department of Electrical Engineering, Stanford University, Stanford, CA 94309 Kunle Olukotun EMAIL Department of Electrical Engineering, Stanford University, Stanford, CA 94309 Christopher R e EMAIL Department of Computer Science, Stanford University, Stanford, CA 94309 |
| Pseudocode | Yes | Algorithm 1 Gibbs sampling Require: Variables xi for 1 i n, and distribution Ī. for t = 1 to T do Sample s uniformly from {1, . . . , n}. Re-sample xs uniformly from PĪ(Xs|X{1,...,n}\{s}). end for |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or an explicit statement of code release. |
| Open Datasets | Yes | we ran HOGWILD!-Gibbs sampling on a real-world 11 GB Knowledge Base Population dataset (derived from the TAC-KBP challenge) |
| Dataset Splits | No | The paper describes the datasets used (e.g., synthetic Ising model graph, KBP dataset) but does not provide specific details on how these datasets were split into training, validation, or test sets. |
| Hardware Specification | Yes | using a machine with a single-socket, 18-core Xeon E7-8890 CPU and 1 TB RAM. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | we simulated HOGWILD!-Gibbs sampling running on a random synthetic Ising model graph of order n = 1000, degree = 3, inverse temperature β = 0.2, and prior weights Ex = 0. |