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].
Differentially Private Clustering via Maximum Coverage
Authors: Matthew Jones, Huy L. Nguyen, Thy D Nguyen11555-11563
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. |
| Researcher Affiliation | Academia | Matthew Jones, Huy L. Nguyen, Thy D Nguyen Northeastern University EMAIL |
| Pseudocode | Yes | Algorithm 1: Maximum Coverage |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes theoretical algorithms and does not mention using any specific publicly available datasets or provide access information for data. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments with data, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental execution, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental execution, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithms, not empirical experiments. Therefore, it does not provide specific experimental setup details like hyperparameters or training configurations. |