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..
The Fast Johnson-Lindenstrauss Transform Is Even Faster
Authors: Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we give a surprising new analysis of the Fast JL transform, showing that the k ln2 n term in the embedding time can be improved to (k ln2 n)/α for an α = Ω(min{ε 1 ln(1/ε), ln n}). The improvement follows by using an even sparser matrix. We complement our improved analysis with a lower bound showing that our new analysis is in fact tight. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aarhus University, Aarhus, Denmark. |
| Pseudocode | No | No pseudocode or algorithm blocks were found. The paper primarily focuses on theoretical analysis, proofs, and mathematical derivations. |
| Open Source Code | No | The paper does not contain any statements about making source code open-source or providing links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset usage. Thus, no information regarding training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical analysis. It does not mention any specific software dependencies or versions required to replicate its findings. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup. Therefore, no experimental setup details like hyperparameters or training settings are provided. |