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 Decomposition via Simultaneous Power Iteration
Authors: Po-An Wang, Chi-Jen Lu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show how to find the eigenvectors simultaneously with the help of a new initialization procedure. This allows us to achieve a better running time in the batch setting, as well as a lower sample complexity in the streaming setting. Our algorithm is given in Algorithm 1, which consists of two phases: the initialization phase and the tensor power phase. Due to the space limitation, we will move all our proofs to the appendix in the supplementary material. |
| Researcher Affiliation | Academia | Po-An Wang 1 Chi-Jen Lu 1 1Academia Sinica, Taiwan. |
| Pseudocode | Yes | Algorithm 1 Robust tensor power method |
| Open Source Code | No | The paper is theoretical and does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper discusses theoretical models of data (e.g., 'noisy version of the tensor', 'stream of vectors') but does not specify a publicly available dataset used for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, such as hyperparameters or training configurations. |