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..
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem
Authors: Bar Mahpud, Or Sheffet
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we test our algorithm empirically and discuss open problems. |
| Researcher Affiliation | Academia | Bar Mahpud Or Sheffet Faculty of Engineering Bar-Ilan University, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 Non-Private Minimum Enclosing Ball |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In the Supplementary Material |
| Open Datasets | Yes | Dataset available freely on archive.ics.uci.edu/ml/datasets/ Bar+Crawl%3A+Detecting+Heavy+Drinking. |
| Dataset Splits | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Hardware Specification | No | The paper states that the total amount of compute and type of resources used were included ([Yes] in self-evaluation), but these specific details are not present in the provided main paper text to be quoted. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |