Fair k-Centers via Maximum Matching
Authors: Matthew Jones, Huy Nguyen, Thy Nguyen
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of the experiments suggest that the algorithm in troduced in this paper shows an improved runtime over exist ing algorithms for the same problem without sacrificing the quality of the result. |
| Researcher Affiliation | Academia | 1Khoury College of Computer Science, Northeastern University, Boston, Massachusetts, U.S.A.. Cor respondence to: Matthew Jones <jones.m@northeastern.edu>, Huy Lê Nguyên <hu.nguyen@northeastern.edu>, Thy Nguyen <nguyen.thy2@northeastern.edu>. |
| Pseudocode | Yes | Algorithm 1 Gonzalez s 2-approximation for k-centers |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their own methodology. |
| Open Datasets | Yes | Adult (A) dataset (Kohavi & Becker, 1996), Wholesale (W) dataset (Cortez, 2014), Student (S) dataset (Cardoso, 2014) |
| Dataset Splits | No | The paper does not explicitly mention training, validation, and test dataset splits with specific percentages or counts. |
| Hardware Specification | Yes | For each algorithm, we collect objective values and run time on every dataset for 100 random runs (performed on a PC with 3.7 GHz i3 / 8GB DDR4). |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies used in their implementation. |
| Experiment Setup | Yes | We generate m 4-dimensional Gaussian isotropic blobs with identity covariance matrix. The total number of points is fixed at 4000 and divided equally among each blob. Each point is randomly assigned to a group. The total number of group is m. We set kf = 1 for all f, to require the algorithms to output m centers satisfying the constraint that one member from each group is represented. |