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 [1].
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. |