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..
Approximation Algorithms for Fair Range Clustering
Authors: Sedjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian
ICML 2023 | Venue PDF | 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 <EMAIL>, Ali Vakilian <EMAIL>. |
| 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. |