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 [1].
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Authors: Zengfeng Huang
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new space-optimal algorithms with faster running times and also show that the running times of our algorithms can be improved if and only if the state-of-the-art running time of matrix multiplication can be improved significantly. |
| Researcher Affiliation | Academia | Zengfeng Huang EMAIL School of Data Science Fudan University Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Dense Shrink Algorithm 2 Dense Shrink R Algorithm 3 FDShrink Algorithm 4 FFDdense Algorithm 5 Weak Low Rank Approximation (LRA) Algorithm 6 FFDsparse Algorithm 7 Row Norms |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories. |
| Open Datasets | No | The paper is theoretical in nature, focusing on algorithmic design and complexity analysis for matrix sketching. It does not present empirical results based on specific datasets, thus no open datasets are mentioned or referenced for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using specific datasets. Therefore, there is no mention of dataset splits (e.g., training, validation, test splits) as there are no empirical evaluations conducted. |
| Hardware Specification | No | The paper focuses on theoretical running time complexity (e.g., O(ndk)) rather than empirical performance on specific hardware. There is no mention of any specific CPU, GPU, or other hardware used for experiments. |
| Software Dependencies | No | The paper describes algorithms but does not mention any specific software components, libraries, or programming languages with version numbers required for implementation or reproduction. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their complexity bounds. It does not include an experimental setup section, hyperparameters, or training configurations for any empirical evaluation. |