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..
Tensor Decomposition via Joint Matrix Schur Decomposition
Authors: Nicolo Colombo, Nikos Vlassis
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our algorithm is faster and at least as accurate and robust than state-of-the-art algorithms for this problem. 4. Experiments 4.1. Comparison on synthetic data Figure 1. Decomposition of a symmetric nonorthogonal tensor. |
| Researcher Affiliation | Collaboration | Nicol o Colombo EMAIL Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur Alzette, Luxembourg Nikos Vlassis EMAIL Adobe Research, San Jose, CA |
| Pseudocode | No | The paper describes the Gauss-Newton algorithm mathematically and verbally in Section 3, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the proposed methodology is publicly available. |
| Open Datasets | Yes | To test the performance of our algorithm on real-world data we have chosen a label prediction problem from crowdsourcing data. The problem and the dataset are described by Zhang et al. (2014) where an estimator based on orderthree moments is also proposed. |
| Dataset Splits | No | The paper discusses generating synthetic data and using a real-world dataset from a cited work, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, counts, or predefined split references). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab codes' for comparison methods but does not provide specific version numbers for Matlab or any other software dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the algorithmic steps and initialization strategy for the Gauss-Newton algorithm, but it does not provide specific numerical values for hyperparameters (e.g., learning rate, batch size, number of iterations/epochs) or detailed convergence criteria for the experimental setup. |