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..
Clustering with Bregman Divergences: an Asymptotic Analysis
Authors: Chaoyue Liu, Mikhail Belkin
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we verify our results, especially centroid s location distribution Eq.(24), by using the Bregman hard clustering algorithm. Figure 1 shows, in the ο¬rst row, the theoretical prediction of distribution of centroids, and in the second row, experimental histograms of centroid locations for different Bregman quantization problems. |
| Researcher Affiliation | Academia | Chaoyue Liu, Mikhail Belkin Department of Computer Science & Engineering The Ohio State University |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. It references 'Algorithm 1 in [3]', but that is external to this paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper describes sampling data points from a uniform distribution ('Suppose the density P is uniform over [0, 1].', 'The density P = U([0, 1]2)') for its experiments. It does not refer to a publicly available dataset with specific access information (link, DOI, citation). |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It discusses sampling data and applying clustering, but no traditional data splitting is described. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud computing resources used for experiments. |
| Software Dependencies | No | The paper mentions applying 'standard k-means', 'Kullback-Leibler clustering', and 'norm-like clustering' algorithms, but does not provide specific version numbers for any software or libraries used (e.g., Python version, library versions). |
| Experiment Setup | Yes | We set number of clusters k = 81, and apply different versions of Bregman hard clustering algorithm on this sample: standard k-means, Kullback-Leibler clustering and norm-like clustering. In addition, we only verify r = 1 cases here, since the Bregman clustering algorithm, which utilizes Lloyd s method, cannot address Bregman quantization problems with r = 1. |