A Nearly-Linear Time Framework for Graph-Structured Sparsity
Authors: Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical analysis with experiments showing that our algorithms also improve on prior work in practice. |
| Researcher Affiliation | Academia | Chinmay Hegde Iowa State University chinmay@iastate.edu Piotr Indyk MIT indyk@mit.edu Ludwig Schmidt MIT ludwigs@mit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Algorithmic steps are described in text. |
| Open Source Code | No | The paper mentions that 'The implementations were supplied by the authors' for the comparison methods, but it does not provide any explicit statement or link to the source code for their own proposed methodology (Graph-Co Sa MP). |
| Open Datasets | No | We perform several experiments with varying oversampling ratios n/s and three different images. See the supplementary material of the full paper [Hegde et al., 2015b] for a description of the dataset, experiments with noise, and a comparison with the graph Lasso. While images are mentioned, the paper does not provide concrete access information (e.g., a specific link, DOI, repository, or formal citation with authors and year for a publicly available dataset) in the main text. |
| Dataset Splits | No | The paper describes the evaluation procedure and mentions '50 trials' for averaging, but it does not explicitly provide details about training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud computing instance types used for running its experiments. It only discusses 'recovery times' in relation to problem size. |
| Software Dependencies | No | The paper mentions that 'The implementations were supplied by the authors' for comparison methods (Struct OMP, La MP, Basis Pursuit, Co Sa MP), but it does not specify any software names with version numbers for either the comparison methods or their own Graph-Co Sa MP algorithm. |
| Experiment Setup | No | The paper mentions using 'default parameter settings' for the comparison methods, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or other training configurations for their own proposed algorithm (Graph-Co Sa MP). |