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 Clustering: Tight Approximation Ratios
Authors: Badih Ghazi, Ravi Kumar, Pasin Manurangsi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the task of differentially private clustering. For several basic clustering problems, including Euclidean Densest Ball, 1-Cluster, k-means, and k-median, we give efficient differentially private algorithms that achieve essentially the same approximation ratios as those that can be obtained by any non-private algorithm, while incurring only small additive errors. |
| Researcher Affiliation | Industry | Badih Ghazi Google Research Mountain View, CA EMAIL; Ravi Kumar Google Research Mountain View, CA EMAIL; Pasin Manurangsi Google Research Mountain View, CA EMAIL |
| Pseudocode | Yes | Algorithm 1: DENSESTBALL (x1, . . . , xn; r, ) |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology or provide a link to a code repository. |
| Open Datasets | No | The paper defines abstract input data ('a set X of n points, each contained in the d-dimensional unit ball') for its theoretical algorithms but does not mention the use of any specific publicly available or open datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments, therefore there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | The paper describes theoretical algorithms and their properties, but it does not specify any hardware used for running experiments as no empirical experiments are reported. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis, and thus does not specify any software dependencies with version numbers for reproducing empirical experiments. |
| Experiment Setup | No | The paper is theoretical and presents algorithm design and analysis; therefore, it does not include details about an experimental setup, hyperparameters, or training configurations. |