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..
OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
Authors: Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Multiple experiments conducted on real datasets of various sizes and dimensions show that our algorithm provides similar performances as state-of-the-art methods such as Faster PAM and Bandit PAM++ with a drastically reduced running time. |
| Researcher Affiliation | Collaboration | 1Centre Borelli, Universit e Paris-Saclay, CNRS, ENS Paris-Saclay 2Michelin |
| Pseudocode | No | The paper describes algorithms using equations and textual explanations, but no explicitly labeled 'Pseudocode' or 'Algorithm' block is present. |
| Open Source Code | Yes | Code https://github.com/antoinedemathelin/obpam |
| Open Datasets | Yes | We conduct the experiments on the MNIST and CIFAR10 image datasets (Le Cun, Cortes, and Burges 1994; Krizhevsky, Hinton et al. 2009) and 8 UCI datasets (Dua and Graff 2017) |
| Dataset Splits | No | The paper mentions dividing datasets into 'small scale' and 'large scale' categories for experimental purposes, but does not provide specific train/test/validation splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | Yes | The experiments are run on a 8G RAM computer with 4 cores. |
| Software Dependencies | No | The paper states: 'Our implementation of One Batch PAM is coded in Python with the Cython module.' and refers to 'official implementations of Bandit PAM++' and 'Python library kmedoids4'. However, no specific version numbers for Python, Cython, Bandit PAM++, or kmedoids are provided. |
| Experiment Setup | Yes | Experiments are performed for different values of k in {10, 50, 100}. Each experiment is repeated 5 times to compute the standard deviations. For Bandit PAM++, we consider the three different settings of swap iterations: T {0, 2, 5}. For Faster CLARA we consider two different settings for the number of subsampling repetitions: I {5, 50}. The sample size is set to m = 80 + 4k as suggested in (Schubert and Rousseeuw 2021). Three different chain lengths are considered for kmc2: L = {20, 100, 200} and two different number of local search iterations for LS-kmeans++: Z = {5, 10}. For One Batch PAM, we use a sample size of m = 100 log(kn). |