Bicriteria Approximation Algorithms for the Submodular Cover Problem
Authors: Wenjing Chen, Victoria Crawford
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithms are then demonstrated to be effective in an experimental section on data summarization and graph cut, two applications of SCP. |
| Researcher Affiliation | Academia | Wenjing Chen, Victoria G. Crawford Department of Computer Science & Engineering Texas A&M University jj9754@tamu.edu, vcrawford@tamu.edu |
| Pseudocode | Yes | Algorithm 1 stoch-greedy-c |
| Open Source Code | No | The paper mentions pseudocode in the supplementary material but does not provide explicit statements or links for the availability of open-source code for the described methodologies. |
| Open Datasets | Yes | The data summarization instance featured here in the main paper is the delicious dataset of URLs tagged with topics, and f takes a subset of URLs to the number of distinct topics represented by those URLs (n = 5000 with 8356 tags) [Soleimani and Miller, 2016]. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | We run the algorithms with input ϵ in the range (0, 0.15) and threshold values between 0 and f(U) (f(U) is the total number of tags). When ϵ is varied, τ is fixed at 0.6f(U). When τ is varied, ϵ is fixed at 0.2. The parameter α is set to be 0.1 and the initial guess of |OPT| for stoch-greedy-c and convert-rand is set to be τ/ maxs f(s). |