Vector-Space Markov Random Fields via Exponential Families
Authors: Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach via a set of synthetic data experiments as well as a realworld case study of over four million foods from the popular diet tracking app My Fitness Pal. Our results demonstrate that our algorithm performs well empirically and that VS-MRFs are capable of capturing and highlighting interesting structure in complex, real-world data. |
| Researcher Affiliation | Academia | Wesley Tansey TANSEY@CS.UTEXAS.EDU Department of Computer Science, The University of Texas, Austin, TX 78712, USA Oscar Hernan Madrid Padilla OSCAR.MADRID@UTEXAS.EDU Department of Statistics and Data Sciences, The University of Texas, Austin, TX 78712, USA Arun Sai Suggala ARUNSAI@CS.UTEXAS.EDU Pradeep Ravikumar PRADEEPR@CS.UTEXAS.EDU Department of Computer Science, The University of Texas, Austin, TX 78712, USA |
| Pseudocode | No | Section 4.1, "Optimization Procedure," describes the steps for ADMM updates in paragraph form and with equations, but it does not include a structured pseudocode block or algorithm labeled as such. |
| Open Source Code | Yes | All code for our algorithm is open source and publicly available. ... All code for our VS-MRF implementation is publicly available.5 [footnote 5 points to https://github.com/tansey/vsmrfs] |
| Open Datasets | No | The paper mentions using a "real-world case study of over four million foods from the popular diet tracking app My Fitness Pal" and "synthetic data experiments". While it refers to a dataset (My Fitness Pal), it does not provide concrete access information (link, citation for public access, repository) to this dataset itself. |
| Dataset Splits | No | The paper does not explicitly state training, validation, or test dataset splits with specific percentages or counts. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper describes the use of ADMM (Alternating Direction Method of Multipliers) but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | For each experiment, we conducted 30 independent trials by generating random weights and sampling via Gibbs sampling with a burn-in of 2000 and thinning step size of 10. We consider two different sparsity scenarios: high (90% edge sparsity, 50% intra-edge parameter sparsity) and low (50% edge sparsity, 10% intra-edge parameter sparsity). ... We use AND graph stitching and measure the true positive rate (TPR) and false positive rate (FPR) as the number of samples increases from 100 to 25K. |