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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |