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..
Bicriteria Approximation Algorithms for the Submodular Cover Problem
Authors: Wenjing Chen, Victoria Crawford
NeurIPS 2023 | Venue PDF | 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 EMAIL, EMAIL |
| 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). |