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..
Differentially Private Correlation Clustering
Authors: Mark Bun, Marek Elias, Janardhan Kulkarni
ICML 2021 | Venue PDF | 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. |