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].
Subspace Selection via DR-Submodular Maximization on Lattices
Authors: So Nakashima, Takanori Maehara4618-4625
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We veri๏ฌed this idea by conducting numerical experiments on a synthetic dataset; see Appendix C. |
| Researcher Affiliation | Academia | 1The University of Tokyo, 2RIKEN Center for Advanced Intelligence Project |
| Pseudocode | Yes | Algorithm 1 Greedy algorithm for monotone height constrained problem. Algorithm 2 Greedy algorithm for monotone knapsack constrained problem. Algorithm 3 Double-greedy algorithm for non-monotone unconstrained problem. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'numerical experiments on a synthetic dataset' but does not provide access information (link, citation, or repository) for this dataset. |
| Dataset Splits | No | The paper mentions 'numerical experiments on a synthetic dataset' but does not provide any specific details about train/validation/test splits, percentages, or methodology for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | No | The paper states that numerical experiments were conducted on a synthetic dataset (Appendix C), but it does not provide specific experimental setup details like hyperparameter values, model initialization, or training configurations within the main text. |