Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Authors: Lin Chen, Moran Feldman, Amin Karbasi
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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. |