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..
Random Cuts are Optimal for Explainable k-Medians
Authors: Konstantin Makarychev, Liren Shan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the RANDOMCOORDINATECUT algorithm gives the optimal competitive ratio for explainable k-medians in ℓ1. ... We provide a tight analysis of the algorithm and prove that its competitive ratio is upper bounded by 2 ln k + 2. |
| Researcher Affiliation | Academia | Konstantin Makarychev Northwestern University Liren Shan TTIC |
| Pseudocode | Yes | Figure 2: RANDOMCOORDINATECUT algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not mention any specific datasets for training or provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, or testing). |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training settings. |