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..
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Authors: Ricardo Henao, Xin Yuan, Lawrence Carin
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive set of experiments demonstrate the utility of using a nonlinear Bayesian SVM within discriminative feature learning and factor modeling, from the standpoints of accuracy and interpretability. |
| Researcher Affiliation | Academia | Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 EMAIL |
| Pseudocode | No | The paper describes algorithms and inference procedures in detail using prose and mathematical equations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper states 'All code used in the experiments was written in Matlab...' but does not provide any explicit statement about making the code open-source or offer a link to a code repository. |
| Open Datasets | Yes | We first compare the performance of the proposed Bayesian hierarchy for nonlinear SVM (BSVM) against EP-based GP classification (GPC) and an optimization-based SVM, on six well known benchmark datasets. (...) USPS handwritten digits dataset, consisting of 1540 gray scale 16 16 images (...) The dataset originally introduced in [24] consists of gene expression measurements from primary breast tumor samples... |
| Dataset Splits | Yes | The parameters of the SVM {γ, θ} are obtained by grid search using an internal 5-fold cross-validation. (...) validation is done by 10-fold cross-validation. |
| Hardware Specification | Yes | All code used in the experiments was written in Matlab and executed on a 2.8GHz workstation with 4Gb RAM. |
| Software Dependencies | No | The paper states that the code was written in 'Matlab' but does not specify any version numbers for Matlab or for any other software libraries or dependencies used. |
| Experiment Setup | Yes | In all experiments we set the covariance function to (i) either the square exponential (SE)... or (ii) the automatic relevance determination (ARD) SE... (...) The parameters of the SVM {γ, θ} are obtained by grid search using an internal 5-fold cross-validation. (...) For our model we set 200 as the maximum number of iterations of the ECM algorithm and run ML-II every 20 iterations. (...) For inference, we set K = 10, a SE covariance function and run the sampler for 1200 iterations, from which we discard the first 600 and keep every 10-th for posterior summaries. |