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..
k-variates++: more pluses in the k-means++
Authors: Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of our analysis via experimental evaluation on several domains and settings, displaying competitive performances vs state of the art. Section 5 presents experimental results. |
| Researcher Affiliation | Collaboration | Data61, Sydney, Australia & { The Australian National University; The University of New South Wales} Ecole Polytechnique, Palaiseau, France & { The Technion, Haifa, Israel; Sony CS Labs, Inc., Tokyo, Japan} |
| Pseudocode | Yes | Algorithm 0 k-variates++ ... Algorithm 1 Dk-means++ ... Algorithm 2 Sk-means++ ... Algorithm 3 OLk-means++ |
| Open Source Code | No | The paper refers to a Supplementary Information (Nock et al., 2016a) for proofs and extensive experiments, but does not provide a direct link to any open-source code repository for the methodology. |
| Open Datasets | No | The paper mentions datasets like 'Life Sci', 'Image', 'Europe Diff' in Table 2 and 'synthetic data', but does not provide concrete access information (e.g., specific links, DOIs, or citations with authors/year) for public availability. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits, percentages, or cross-validation details needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory details) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components used in the experiments. |
| Experiment Setup | Yes | Parameters are in line with (Bahmani et al., 2012). ... We let ϵ = 1 in our experiments. ... each peer s initial data consists of points uniformly sampled in a random hyperrectangle in a space of d = 50 (expected number of peers points mi = 500, i). We sample peers until a total of m 20000 point is sampled. |