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..
Nearly Isometric Embedding by Relaxation
Authors: James McQueen, Marina Meila, Dominique Joncas
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments confirm the superiority of our algorithm in obtaining low distortion embeddings. 4 Experimental evaluation |
| Researcher Affiliation | Collaboration | James Mc Queen Department of Statistics University of Washington Seattle, WA 98195 EMAIL Marina Meil a Department of Statistics University of Washington Seattle, WA 98195 EMAIL Dominique Perrault-Joncas Google Seattle, WA 98103 EMAIL |
| Pseudocode | Yes | Algorithm 1: Outline of the Riemannian Relaxation Algorithm. Algorithm 2: RIEMANNIANRELAXATION (RR) Algorithm 3: PRINCIPALCURVES-RIEMANNIANRELAXATION (PCS-RR) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its own source code or a link to a repository for the methodology described. |
| Open Datasets | Yes | The data consists of spectra of galaxies from the Sloan Digital Sky Survey7 [1]. 7 www.sdss.org |
| Dataset Splits | No | The paper describes total sample sizes used in experiments (e.g., n = 10000, n = 3000, n = 2000 subsample) but does not provide explicit training, validation, or test set split percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments, such as GPU or CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions the use of 'drtoolbox' for computing embeddings of other algorithms, but it does not specify version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | Algorithm 2 mentions 'heavy ball parameter α [0, 1)' and finding 'step size η by line search'. Section 4 states 'Convergence of RR was achieved after 400 iterations' and 'Convergence of PCS-RR was achieved after 1000 iterations'. |