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..
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
Authors: Quanming Yao, James Tin-Yau Kwok, Bo Han
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a number of synthetic and real-world data sets show that the proposed algorithm is more efficient in both time and space, and is also more accurate than existing approaches. |
| Researcher Affiliation | Collaboration | 1 Paradigm Inc, Beijing, China 2 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 3 Center for Advanced Intelligence Project, RIKEN, Japan. |
| Pseudocode | Yes | Algorithm 1 NOnconvex Regularized Tensor (NORT). |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We use windows, tree and rice from (Hu et al., 2013),... Experiments are performed on three hyper-spectral data sets (Appendix C.3): Cabbage (1312 432 49), Scene (1312 951 49) and Female (592 409 148)... on the You Tube data set (Lei et al., 2009). |
| Dataset Splits | Yes | We use 50% of them for training, and the remaining 50% for validation. Testing is evaluated on the unobserved elements in O. (Synthetic Data) ... We randomly sample 10% of the pixels for training, which are then corrupted by Gaussian noise N(0, 0.01). Half of the training pixels are used for validation. (Color Images) ... We use 50% of the observations for training, another 25% for validation and the rest for testing. (Social Networks) |
| Hardware Specification | Yes | Experiments are performed on a PC with Intel-i8 CPU and 32GB memory. |
| Software Dependencies | No | The paper mentions implementation in Matlab and C, but does not provide specific version numbers for these software dependencies or any libraries used. |
| Experiment Setup | Yes | For NORT, τ has to be larger than ρ + DL (Corollary 3.6). However, a large τ leads to slow convergence (Remark 3.2). Hence, we set τ = 1.01(ρ+DL). Moreover, we set γ1 = 0.1 and p = 0.5 as in (Li et al., 2017). Besides, Fτ in step 5 of Algorithm 1 is hard to evaluate, and we use F instead as in (Zhong & Kwok, 2014). |