A Complete Recipe for Stochastic Gradient MCMC
Authors: Yi-An Ma, Tianqi Chen, Emily Fox
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on simulated data and a streaming Wikipedia analysis demonstrate that the proposed SGRHMC sampler inherits the benefits of Riemann HMC, with the scalability of stochastic gradient methods. |
| Researcher Affiliation | Academia | Yi-An Ma, Tianqi Chen, and Emily B. Fox University of Washington {yianma@u,tqchen@cs,ebfox@stat}.washington.edu |
| Pseudocode | Yes | Algorithm 1: Generalized Stochastic Gradient Riemann Hamiltonian Monte Carlo |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that the code for the methodology described is available. |
| Open Datasets | No | The paper mentions "a streaming Wikipedia analysis using latent Dirichlet allocation" and "latent Dirichlet allocation (LDA) model on a large Wikipedia dataset". While Wikipedia is public, no specific link, DOI, or formal citation for the dataset used for their analysis is provided to ensure reproducibility. |
| Dataset Splits | No | The paper mentions "simulated data" and a "streaming Wikipedia analysis" but does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts) for these experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific frameworks) used in the experiments. |
| Experiment Setup | No | The paper states: "The Supplement contains details on the specific samplers considered and the parameter settings used in these experiments." However, no specific hyperparameters or detailed training configurations are provided within the main body of the paper. |