Fair k-Center Clustering for Data Summarization
Authors: Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |