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..
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Authors: Lin Chen, Moran Feldman, Amin Karbasi
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, our experimental results show that our proposed algorithm performs well in a variety of real-world problems, including regression, video summarization, splice site detection, and black-box interpretation. |
| Researcher Affiliation | Academia | 1Yale Institute for Network Science, Yale University, New Haven, CT, USA 2Department of Electrical Engineering, Yale University 3 Department of Mathematics and Computer Science, Open University of Israel, Ra anana, Israel. |
| Pseudocode | Yes | Algorithm 1 Residual Random Greedy for Matroids |
| Open Source Code | No | The paper does not provide explicit access (link, statement of release) to its own source code. |
| Open Datasets | Yes | A detailed description of this dataset is presented in (Yeo & Burge, 2004). |
| Dataset Splits | No | The paper describes the characteristics and generation of datasets used (e.g., n=100, p=200 for synthetic data; MEMset dataset details) but does not specify explicit training, validation, or test splits (e.g., 80/10/10%). |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like "logistic regression", "LIME framework", and "SLIC algorithm" but does not provide specific version numbers for any of these or other dependencies. |
| Experiment Setup | Yes | We chose n = 100 and p = 200, and constructed each row of the n p matrix X independently according to an autoregressive (AR) process with α = 0.5 and noise variance σ2 = 10. |