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..
Sparse Approximate Conic Hulls
Authors: Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide experimental results for the convex and conic algorithms on a variety of feature selection tasks. |
| Researcher Affiliation | Academia | Gregory Van Buskirk, Benjamin Raichel, and Nicholas Ruozzi Department of Computer Science University of Texas at Dallas Richardson, TX 75080 EMAIL |
| Pseudocode | Yes | Algorithm 1: Greedy Conic Hull |
| Open Source Code | No | The paper does not provide explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | For our experiments, we considered the performance of each of the methods when used to select features for a variety of SVM classification tasks on various image, text, and speech data sets including several from the Arizona State University feature selection repository [Li et al., 2016] as well as the UCI Reuters dataset and the BBC News dataset [Greene and Cunningham, 2006]. |
| Dataset Splits | Yes | For each problem, the data is divided using a 30/70 train/test split, the features are selected by the indicated method, and then an SVM classifier is trained using only the selected features. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like SVM classification but does not provide specific version numbers for any software or libraries. |
| Experiment Setup | Yes | For the conic and convex hull methods, ϵ is set to 0.1. |