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..
Optimal Cluster Recovery in the Labeled Stochastic Block Model
Authors: Se-Young Yun, Alexandre Proutiere
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We find the set of parameters such that there exists a clustering algorithm with at most s misclassified items in average under the general LSBM and for any s = o(n), which solves one open problem raised in [2]. We further develop an algorithm, based on simple spectral methods, that achieves this fundamental performance limit within O(npolylog(n)) computations and without the a-priori knowledge of the model parameters. |
| Researcher Affiliation | Collaboration | Se-Young Yun CNLS, Los Alamos National Lab. Los Alamos, NM 87545 EMAIL Alexandre Proutiere Automatic Control Dept., KTH Stockholm 100-44, Sweden EMAIL |
| Pseudocode | Yes | The SP algorithm consists in two parts, and its detailed pseudo-code is presented at the beginning of the supplementary document (see Algorithm 1). |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical models (Labeled Stochastic Block Model) rather than empirical evaluation on real-world datasets. No datasets, public or otherwise, are mentioned. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm complexity, not empirical execution. Therefore, it does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers required for reproducibility. |
| Experiment Setup | No | The paper is theoretical, presenting an algorithm and its mathematical analysis, rather than empirical experiments. Therefore, it does not include details on an experimental setup, hyperparameters, or training configurations. |