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..
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Authors: Matthew Fahrbach, Gang Fu, Mehrdad Ghadiri
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the speed and accuracy of this Kronecker regression algorithm on synthetic data and real-world image tensors. 6 Experiments All experiments were run using Num Py [26] with an Intel Xeon W-2135 processor (8.25MB cache, 3.70 GHz) and 128GB of RAM. The Fast Kronecker Regression-based ALS experiments for low-rank Tucker decomposition on image tensors are deferred to Appendix D.2. |
| Researcher Affiliation | Collaboration | Matthew Fahrbach Google Research EMAIL Gang Fu Google Research EMAIL Mehrdad Ghadiri Georgia Tech EMAIL |
| Pseudocode | Yes | Algorithm 1 Tucker ALS, Algorithm 2 Fast Kronecker Regression, Algorithm 3 Fast Factor Matrix Update |
| Open Source Code | Yes | All of our code is available at https://github.com/fahrbach/subquadratic-kronecker-regression. |
| Open Datasets | Yes | We used two real-world image tensors to evaluate the performance of our ALS implementation with Fast Kronecker Regression: the Columbia Object Image Library (COIL-20) [51] and the Natural Scenes Dataset (NSD) [50]. |
| Dataset Splits | No | The paper mentions using synthetic data and specific image tensors (COIL-20, NSD) but does not provide details on how these datasets were split into training, validation, or test sets, or reference standard splits. |
| Hardware Specification | Yes | All experiments were run using Num Py [26] with an Intel Xeon W-2135 processor (8.25MB cache, 3.70 GHz) and 128GB of RAM. |
| Software Dependencies | No | The paper mentions using "Num Py [26]" for experiments but does not provide specific version numbers for NumPy or any other software dependencies. |
| Experiment Setup | Yes | For both sketching algorithms, we use " = 0.1 and δ = 0.01. We reduce the number of row samples in both algorithms by = 10 5 so that the algorithms are more practical and comparable to the earlier experiments in [17, 18]. Lastly, we set λ = 10 3. |