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..
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. |