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..
Sparse and Low-Rank Tensor Decomposition
Authors: Parikshit Shah, Nikhil Rao, Gongguo Tang
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our algorithm with numerical experiments. |
| Researcher Affiliation | Collaboration | Parikshit Shah EMAIL Nikhil Rao EMAIL Gongguo Tang EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for sparse and low rank tensor decomposition |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | A tensor Z is generated as the sum of a low rank tensor X and a sparse tensor Y . The low-rank component is generated as follows: Three sets of r unit vecots ui, vi, wi R50 are generated randomly, independently and uniformly distributed on the unit sphere. Also a random positive scale factor (uniformly distributed on [0, 1] is chosen and the tensor X = Pr i=1 λi ui vi wi. The tensor Y is generated by (Bernoulli) randomly sampling its entries with probability p. |
| Dataset Splits | No | The paper describes generating synthetic data for experiments but does not specify explicit train/validation/test splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The optimization problem (6) is solved using CVX in MATLAB. No version numbers are provided for CVX or MATLAB. |
| Experiment Setup | Yes | In all our experiments, the regularization parameter was picked to be ν = 1 n. ... For each such p, we perform 10 trials... We run 5 independent trials |