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].
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. |