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..
Extreme k-Center Clustering
Authors: MohammadHossein Bateni, Hossein Esfandiari, Manuela Fischer, Vahab Mirrokni3941-3949
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an empirical study to corroborate our theoretical guarantees, and demonstrate that the algorithm performs well in practice. |
| Researcher Affiliation | Collaboration | 1Google Research, NYC, New York, USA 2ETH, Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 SAMPLE-AND-SOLVE(V , p, r) |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing source code for the methodology described. |
| Open Datasets | Yes | We employ 3 datasets in the experiments: two publicly available datasets (song (Dheeru and Karra Taniskidou 2017) and en-wiki (Epasto, Mirrokni, and Zadimoghaddam 2017)) and a much larger private one (prod). |
| Dataset Splits | No | The paper describes the datasets used for experiments but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states that experiments were run 'on a cloud platform (similar to Hadoop)' using 'no more than 100 machines', but it does not provide specific hardware details like CPU or GPU models for their algorithms. |
| Software Dependencies | No | The paper mentions that the algorithms were 'implemented... in C++', but does not provide specific version numbers for compilers, libraries, or other software dependencies. |
| Experiment Setup | No | The paper does not provide specific details on experimental setup, such as hyperparameter values or system-level training settings for their proposed algorithms. |