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..
Average Sensitivity of Euclidean k-Clustering
Authors: Yuichi Yoshida, Shinji Ito
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Given a set of n points in Rd, the goal of Euclidean (k, β)-clustering is to ο¬nd k centers that minimize the sum of the β-th powers of the Euclidean distance of each point to the closest center. ... We ο¬rst show that a popular algorithm k-MEANS++ and its variant called Dβ-SAMPLING have low average sensitivity. Next, we show that any approximation algorithm for Euclidean (k, β)-clustering can be transformed to an algorithm with low average sensitivity while almost preserving the approximation guarantee. As byproducts of our results, we provide several algorithms for consistent (k, β)-clustering and dynamic (k, β)-clustering in the random-order model... |
| Researcher Affiliation | Collaboration | Yuichi Yoshida National Institute of Informatics JST, PRESTO EMAIL Shinji Ito NEC EMAIL |
| Pseudocode | Yes | Algorithm 1: Dβ-sampling for Euclidean (k, β)-clustering |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a repository. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |