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..
Multiway clustering via tensor block models
Authors: Miaoyan Wang, Yuchen Zeng
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods. |
| Researcher Affiliation | Academia | Miaoyan Wang University of Wisconsin Madison EMAIL Yuchen Zeng University of Wisconsin Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Multiway clustering based on tensor block models |
| Open Source Code | Yes | Our software is available at https://cran.r-project.org/web/packages/tensorsparse. |
| Open Datasets | Yes | The ο¬rst dataset is a real-valued tensor, consisting of approximate 1 million expression values from 13 brain tissues, 193 individuals, and 362 genes [4]. The second dataset we consider is the Nations data [2]. |
| Dataset Splits | No | The paper mentions conducting simulation studies and applying the method to real datasets but does not provide specific details on train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions that their software is an R package available on CRAN, but it does not specify version numbers for R or any other software dependencies. |
| Experiment Setup | Yes | Unless otherwise stated, we generate Gaussian tensors under the block model (1). The block means are generated from i.i.d. Uniform[-3,3]. The entries in the noise tensor E are generated from i.i.d. N(0, Ο2). In each simulation study, we report the summary statistics across nsim = 50 replications. We set Ο = 3 and consider tensors of order 3 and order 4. |