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..
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Authors: Qinqing Zheng, Ryota Tomioka
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct tensor denoising experiments on synthetic and real datasets, to numerically confirm our analysis in previous sections. |
| Researcher Affiliation | Academia | Qinqing Zheng University of Chicago EMAIL Ryota Tomioka Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | Yes | We use Algorithm 2 described in Section 3. (Refers to Algorithm 1 in Appendix B: Algorithm 1 Tensor Denoising with Subspace Norm) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for the methodology is being released. |
| Open Datasets | Yes | The amino acid dataset [5] is a semi-realistic dataset commonly used as a benchmark for low rank tensor modeling. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., train/validation/test percentages, sample counts, or citations to predefined splits) or cross-validation methodology for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'tensorlab [22]' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | The CP decomposition is computed by the tensorlab [22] with 20 random initializations. We assumed CP knows the true rank is 2. For the subspace norm, we use Algorithm 2 described in Section 3. We also select the top 2 singular vectors when constructing b U (k) s. We computed the solutions for 20 values of regularization parameter λ logarithmically spaced between 1 and 100. For the overlapped and the latent norm, we use ADMM described in [25]; we also computed 20 solutions with the same λ s used for the subspace norm. |