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..
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss
Authors: Aleksandros Sobczyk, Mathieu Luisier
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Proof-of-concept numerical experiments are presented to validate the theoretical analysis. |
| Researcher Affiliation | Collaboration | Aleksandros Sobczyk IBM Research and ETH Zürich Zürich, Switzerland EMAIL Mathieu Luisier ETH Zürich Zürich, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Euclidean Norm Estimation |
| Open Source Code | No | No because the code is proprietary. |
| Open Datasets | No | The paper discusses generating 'synthetic matrices' and provides details on their construction but does not refer to any publicly available datasets or provide access information for the generated data. |
| Dataset Splits | No | The paper describes numerical experiments, but it does not specify any training/test/validation splits. It discusses generating synthetic matrices and running experiments, but no explicit data partitioning is mentioned. |
| Hardware Specification | No | Our small scale indicative experiments were ran on a small laptop. |
| Software Dependencies | No | Algorithm 1 was implemented in Python using Num Py. |
| Experiment Setup | Yes | Specifically, d d matrices A, with d = 5000, were created as follows. We drew a random orthogonal d d matrix Q. We then fixed a diagonal d d matrix Λ which defines the eigenvalues of the matrix. Each element Λi,i, i [d] is set to i c for a given c 0. The larger the c, the faster the spectral decay. We finally constructed the symmetric A = QΛQ which were used in the numerical experiments. Following [34], we applied four different decay factors, specifically c = {0.5, 1, 1.5, 2}. Therefore, we set G, S, and Q in Algorithm 1 to have size d m/4, so that both algorithms are tested with the same number of matrix-vector products. |