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..
Fair k-Center Clustering for Data Summarization
Authors: Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a number of experiments. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Rutgers University, NJ 2College of Computing, Georgia Tech, GA. |
| Pseudocode | Yes | Algorithm 1 Approximation algorithm for (3), Algorithm 2 Approximation algorithm for (2) when m = 2, Algorithm 3 Algorithm for exchanging centers & finding G, Algorithm 4 Approximation alg. for (2) for arbitrary m |
| Open Source Code | Yes | Python code is available on https://github.com/matthklein/fair-k-center-clustering. |
| Open Datasets | Yes | Adult data set (Dua & Graff, 2019). |
| Dataset Splits | No | The paper describes splitting datasets into demographic groups (e.g., by gender or race) for fairness considerations, but it does not provide explicit details on train/validation/test splits, percentages, or sample counts necessary for reproduction. |
| Hardware Specification | Yes | performed on an i Mac with 3.4 GHz i5 / 8 GB DDR4. |
| Software Dependencies | No | We implemented the algorithm by Chen et al. (2016) using the generic algorithm for matroid intersection provided in Sage Math. The paper mentions 'Sage Math' but does not specify a version number. |
| Experiment Setup | Yes | We only use its six numerical features (e.g., age, hours worked per week), normalized to zero mean and unit variance, for representing records and use the l1distance as metric d. |