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 Completion Made Practical
Authors: Allen Liu, Ankur Moitra
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe our experimental results and in particular how our algorithm compares to existing algorithms. |
| Researcher Affiliation | Academia | Allen Liu Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL Ankur Moitra Department of Mathematics, Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL |
| Pseudocode | Yes | Algorithm 1 FULL EXACT TENSOR COMPLETION |
| Open Source Code | Yes | The code for all of the experiments can be found in Section I. |
| Open Datasets | No | Uncorrelated tensors: generated by taking T = P4 i=1 xi yi zi where xi, yi, zi are random unit vectors. Correlated tensors: generated by taking T = P4 i=1 0.5i 1xi yi zi where x1, y1, z1 are random unit vectors and for i > 1, xi, yi, zi are random unit vectors that have covariance 0.88 with x1, y1, z1 respectively. |
| Dataset Splits | No | The paper mentions using a random subset of observations for alternating minimization steps but does not provide explicit train/validation/test dataset splits, percentages, or absolute counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or computational resources used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as programming languages, libraries, or frameworks used for the implementation. |
| Experiment Setup | Yes | We ran KRONECKER COMPLETION and STANDARD ALTERNATING MINIMIZATION for n = 200, r = 4 and either 50000 or 200000 observations. We ran 100 trials and took the median normalized MSE... For alternating minimization steps, we use a random subset consisting of half of the observations. We ran KRONECKER COMPLETION for 100 iterations compared to running STANDARD ALTERNATING MINIMIZATION for 400 iterations |