Approximating the Top Eigenvector in Random Order Streams
Authors: Praneeth Kacham, David Woodruff
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 pkacham@google.com David P. Woodruff Carnegie Mellon University dwoodruf@cs.cmu.edu |
| 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. |