Approximation Algorithms for Fair Range Clustering
Authors: Sedjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we provide an efficient constant factor approximation algorithm for the fair range ℓp-clustering for all values of p [1, ). |
| Researcher Affiliation | Collaboration | 1African Institute for Mathematical Sciences Rwanda 2Microsoft Research Redmond 3Toyota Technological Institute at Chicago. Correspondence to: Sepideh Mahabadi <smahabadi@microsoft.com>, Ali Vakilian <vakilian@ttic.edu>. |
| Pseudocode | Yes | Algorithm 1 Partitioning Facilities. ... Algorithm 2 Consolidating Locations. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and presents approximation algorithms and proofs. It does not conduct empirical studies on datasets, thus no training data is used or mentioned as publicly available. |
| Dataset Splits | No | The paper focuses on theoretical algorithm design and analysis, not empirical validation. Therefore, it does not describe validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention specific software dependencies with version numbers required to replicate any experiments. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs. It does not describe an experimental setup with hyperparameters or system-level training settings. |