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..
Fused Orthogonal Alternating Least Squares for Tensor Clustering
Authors: Jiacheng Wang, Dan Nicolae
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate the accuracy and computational efficient implementation of the Fused-Orth-ALS algorithm by using both simulations and real datasets. |
| Researcher Affiliation | Academia | Jiacheng Wang Department of Statistics University of Chicago EMAIL Dan Nicolae Department of Statistics University of Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1: Fused-Orth-ALS Algorithm |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Section 5 and supplementary material |
| Open Datasets | Yes | Experiments are conducted on two real datasets: the brain node structural connectivity from Human Connectome Project (HCP) 1 [25] and political relationships between nations 2 [13]. 1Dataset is available at http://www.humanconnectomeproject.org/ 2Dataset is available at http://www.charleskemp.com/code/irm.html. |
| Dataset Splits | No | The paper states that training details are in supplementary material but does not explicitly provide specific train/validation/test dataset splits (percentages, counts, or predefined splits) in the main text. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplementary material. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers (e.g., 'PyTorch 1.9' or 'scikit-learn 0.24'), in the main text. |
| Experiment Setup | Yes | We set d = 20, d3 = 48, ยต = 1, vary ฯ {0, 0.25, 0.5, 0.75, 1} |