Differentially Private Correlation Clustering
Authors: Mark Bun, Marek Elias, Janardhan Kulkarni
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error of Ω(n). |
| Researcher Affiliation | Collaboration | 1Boston University, Boston 2CWI, Amsterdam 3Microsoft Research, Redmond. |
| Pseudocode | Yes | The paper includes several algorithm blocks labeled 'Algorithm 1', 'Algorithm 2', 'Algorithm 3', 'Algorithm 4', 'Algorithm 5', and 'Algorithm 6'. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical studies involving datasets. Therefore, it does not provide information about publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical studies involving datasets. Therefore, it does not provide information about training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or system-level training settings. |