Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Guarantees for k-means++ and k-means++ Parallel
Authors: Konstantin Makarychev, Aravind Reddy, Liren Shan
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,767 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,689 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-meansk. Our results give a better theoretical justification for why these algorithms perform extremely well in practice. |
| Researcher Affiliation | Academia | Konstantin Makarychev Aravind Reddy Liren Shan Department of Computer Science Northwestern University Evanston, IL, USA |
| Pseudocode | Yes | Algorithm 1 k-means++ seeding Algorithm 2 k-meansk seeding Algorithm 3 k-meansk Pois seeding |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions that k-means++ is used by various libraries but does not state that the authors are releasing their own code. |
| Open Datasets | Yes | Bio Test from KDD-Cup 2004 (Elber, 2004) and COVTYPE from the UCI ML repository (Dua & Graff, 2017) |
| Dataset Splits | No | The paper mentions running algorithms for 50 iterations on datasets but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning into train, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions running algorithms for "50 iterations" but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size) or other training configurations. |