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..
Simple and sharp analysis of k-means||
Authors: Václav Rozhoň
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we first provide a new, simple analysis of k-means|| , thus simplifying known proofs (Bahmani et al., 2012) and (Bachem et al., 2017). In particular, if we denote by µX the mean of X and ϕ the optimal solution, we prove in Section 3 that O(log ϕX(µX) /ϕ ) rounds of the k-means|| algorithm suffice to get expected constant approximation guarantee. |
| Researcher Affiliation | Academia | V aclav Rozhoˇn 1 1ETH, Zurich. Correspondence to: V aclav Rozhoˇn <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 k-means|| overseeding (...) Algorithm 2 k-means|| (Bahmani et al., 2012) |
| Open Source Code | No | The paper is theoretical and focuses on analysis; it does not mention releasing any source code for its method. |
| Open Datasets | No | This is a theoretical paper and does not involve training models on datasets. Therefore, no information on public dataset access is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits (training, validation, test). Therefore, no information on validation splits is provided. |
| Hardware Specification | No | This is a theoretical paper focusing on algorithm analysis; it does not describe experimental setups or mention any hardware specifications. |
| Software Dependencies | No | This is a theoretical paper focusing on algorithm analysis; it does not describe experimental setups or list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper presenting algorithm analysis and proofs, not experimental results. Therefore, no experimental setup details like hyperparameters or training settings are provided. |