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..
Improved Coresets for Euclidean $k$-Means
Authors: Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we improve these bounds to O(min(k3/2 ε 2, k ε 4)) for Euclidean k-means and O(min(k4/3 ε 2, k ε 4)) for Euclidean k-median. In particular, ours is the first provable bound that breaks through the k2 barrier while retaining an optimal dependency on ε. |
| Researcher Affiliation | Collaboration | Vincent Cohen-Addad Google Research Kasper Green Larsen Aarhus University David Saulpic University of Vienna Chris Schwiegelshohn Aarhus University Omar Ali Sheikh-Omar Aarhus University |
| Pseudocode | No | The paper describes algorithmic concepts and procedures but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on proving bounds; it does not mention releasing source code for the described methodology or provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using specific datasets, thus no information on publicly available training datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation details or experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or provide details such as hyperparameters. |