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..
Constant Approximation for Individual Preference Stable Clustering
Authors: Anders Aamand, Justin Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our O(1)-IP stable clustering algorithm against k-means++, which is the empirically best-known algorithm in [1]. We also compare kmeans++ with our optimal algorithm for Min-IP stability. We run experiments on the Adult data set |
| Researcher Affiliation | Collaboration | Anders Aamand MIT EMAIL Justin Y. Chen MIT EMAIL Allen Li MIT EMAIL Sandeep Silwal MIT EMAIL Pattara Sukprasert Databricks EMAIL Ali Vakilian TTIC EMAIL Fred Zhang UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 BALL-CARVING |
| Open Source Code | No | The paper does not provide a direct link to its own open-source code or explicitly state that the code for its methods is publicly available. It only mentions using the 'k-means++ implementation of Scikit-learn package [21]'. |
| Open Datasets | Yes | We run experiments on the Adult data set2 https://archive.ics.uci.edu/ml/datasets/adult; see [18]. For IP stability, we also use four more datasets from UCI ML repositoriy [11] |
| Dataset Splits | No | The paper mentions using various datasets but does not provide specific details on the training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined standard splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments (e.g., GPU models, CPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions 'Python 3' and 'Scikit-learn package [21]' but does not provide specific version numbers for these software dependencies, which are necessary for reproducible descriptions. |
| Experiment Setup | Yes | We use the k-means++ implementation of Scikit-learn package [21]... we set the parameter n_init=1 and then compute the average of many different runs. |