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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Variational Information Maximization for Feature Selection
Authors: Shuyang Gao, Greg Ver Steeg, Aram Galstyan
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 14,709 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,818 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that the proposed method strongly outperforms existing information-theoretic feature selection approaches. Our experiments demonstrate that the proposed method strongly outperforms existing information-theoretic feature selection approaches. We also conduct empirical validation on various datasets and demonstrate that the proposed approach outperforms state-of-the-art information-theoretic feature selection methods. |
| Researcher Affiliation | Academia | Shuyang Gao Greg Ver Steeg Aram Galstyan University of Southern California, Information Sciences Institute EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper mentions that |
| Open Source Code | Yes | Shuyang Gao. Variational feature selection code. http://github.com/Biu Biu Bi LL/ Info Feature Selection. |
| Open Datasets | Yes | We use 17 well-known datasets in previous feature selection studies [5, 12] (all data are discretized). The dataset summaries are illustrated in supplementary Sec. C. We use the average cross-validation error rate on the range of 10 to 100 features to compare different algorithms under the same setting as [12]. Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. [26] Kevin Bache and Moshe Lichman. Uci machine learning repository, 2013. |
| Dataset Splits | Yes | Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. |
| Hardware Specification | No | No specific hardware details (GPU, CPU models, memory, etc.) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were provided. |
| Experiment Setup | Yes | We use the average cross-validation error rate on the range of 10 to 100 features to compare different algorithms under the same setting as [12]. Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. |