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 Balancing on Statistical Manifold
Authors: Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | show in numerical experiments that the proposed algorithm is several orders of magnitude faster than existing ones. |
| Researcher Affiliation | Academia | 1National Institute of Informatics 2JST PRESTO 3RIKEN Brain Science Institute 4Graduate School of Frontier Sciences, The University of Tokyo 5RIKEN AIP 6NIMS. |
| Pseudocode | No | The paper describes the algorithms in prose and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | An implementation of algorithms for matrices and third order tensors is available at: https://github.com/ mahito-sugiyama/newton-balancing |
| Open Datasets | Yes | Hessenberg Matrix. The ο¬rst set of experiments used a Hessenberg matrix, which has been a standard benchmark for matrix balancing (Parlett & Landis, 1982; Knight & Ruiz, 2013). Next, we collected a set of Trefethen matrices from a collection website2, which are nonnegative diagonal matrices with primes. Footnote 2: http://www.cise.ufl.edu/research/sparse/ matrices/ |
| Dataset Splits | No | The paper evaluates algorithm efficiency and convergence on benchmark matrices but does not mention or specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were conducted on Amazon Linux AMI release 2016.09 with a single core of 2.3 GHz Intel Xeon CPU E5-2686 v4 and 256 GB of memory. |
| Software Dependencies | Yes | All methods were implemented in C++ with the Eigen library and compiled with gcc 4.8.31. |
| Experiment Setup | Yes | We measured the residual of a matrix A = (a ij) by the squared norm (A 1 1, A T 1 1) 2, where each entry a ij is obtained as npij in our algorithm, and ran each of three algorithms until the residual is below the tolerance threshold 10 6. |