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..
A posteriori error bounds for joint matrix decomposition problems
Authors: Nicolo Colombo, Nikos Vlassis
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the bound on synthetic data for which the ground truth is known. |
| Researcher Affiliation | Collaboration | Nicolò Colombo Department of Statistical Science University College London EMAIL Nikos Vlassis Adobe Research San Jose, CA EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper states 'We created a set of synthetic problems in which the ground truth is known'. This indicates generated data, not a publicly available dataset with access information. |
| Dataset Splits | No | The paper evaluates an error bound using synthetic data and different algorithms, but it does not specify traditional train/validation/test dataset splits for model training or evaluation, as the experiment is not about training a machine learning model. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Gauss-Newton algorithm [8]' and 'Jacobi algorithm [13] (our implementation)' but does not provide specific software versions for these or any other dependencies. |
| Experiment Setup | Yes | For each set Mσ = { M̂n}N n=1, two approximate joint triangularizers were computed by optimizing (4) using two different iterative algorithms, the Gauss-Newton algorithm [8], and the Jacobi algorithm [13] (our implementation), initialized with the same random orthogonal matrix. ... We considered two settings, N = 5 and N = 100, and several different noise levels obtained by varying the perturbation parameter σ. |