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..
Approximating the Top Eigenvector in Random Order Streams
Authors: Praneeth Kacham, David Woodruff
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is purely theoretical studying space-efficient algorithms for approximating the top eigenvector. We prove all the claims made in the abstract and introduction. |
| Researcher Affiliation | Collaboration | Praneeth Kacham Google Research EMAIL David P. Woodruff Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: Approximate Eigenvector for Streams with no Large Norms |
| Open Source Code | No | The paper is purely theoretical and does not mention releasing any source code. The 'Experimental Result Reproducibility' section in the NeurIPS checklist is marked 'NA', indicating no experiments were conducted. |
| Open Datasets | No | The paper is purely theoretical and does not involve empirical experiments or datasets, thus no publicly available dataset is mentioned. |
| Dataset Splits | No | The paper is purely theoretical and does not involve empirical experiments or datasets, thus no dataset splits for validation are mentioned. |
| Hardware Specification | No | The paper is purely theoretical and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is purely theoretical and does not conduct experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is purely theoretical and does not conduct experiments, thus no experimental setup details like hyperparameters or training settings are provided. |