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].
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] |