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..
Some Properties of Batch Value of Information in the Selection Problem
Authors: Shahaf S. Shperberg, Solomon Eyal Shimony
JAIR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we capitalize on the submodularity results (Theorems 1 and 4) by suggesting a simple compound greedy scheme in Section 3 for near-optimal solution of the selection problem, and compare its performance to the standard greedy algorithms on a wine quality dataset. |
| Researcher Affiliation | Academia | Shahaf S. Shperberg EMAIL Solomon Eyal Shimony EMAIL Dept. of Computer Science Ben-Gurion University of the Negev P.O. Box 653, Beer-Sheva 84105, Israel |
| Pseudocode | No | The paper describes algorithms (e.g., greedy, compound greedy) but does not present them in a structured pseudocode or algorithm block format. The steps are described within paragraphs. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code, nor does it include links to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | The setting for the tests was based on the UCI white wine quality dataset (Cortez, 2009; Cortez, Cerdeira, Almeida, Matos, & Reis, 2009). |
| Dataset Splits | No | The paper mentions that "Each experiment was on a set I of n + 1 randomly picked wines from the dataset, where n was an experimental parameter," but it does not provide specific training/test/validation splits, percentages, or methodology for data partitioning for reproducibility. |
| Hardware Specification | Yes | Runtimes for the algorithms appear in Figure 4, performed on an Intel(R) Core(TM) i7-4700HQ 2.40GHz with 8 GB RAM running Microsoft windows 8.1 x64, using multiplethread implementations. |
| Software Dependencies | No | The software was implemented in C# with optimizations. However, no specific version numbers for C# or any libraries/frameworks used are provided. |
| Experiment Setup | Yes | Each experiment was on a set I of n + 1 randomly picked wines from the dataset, where n was an experimental parameter, and for each wine a random cost Ci was drawn uniformly between 0.01 to 0.1 (assumed to be on the same scale as quality values). |