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..
Continuous Submodular Maximization: Beyond DR-Submodularity
Authors: Moran Feldman, Amin Karbasi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose the first continuous optimization algorithms that achieve a constant factor approximation guarantee for the problem of monotone continuous submodular maximization subject to a linear constraint. We first prove that a simple variant of the vanilla coordinate ascent, called COORDINATE-ASCENT+, achieves a ( e 1 2e 1 ε)-approximation guarantee while performing O(n/ε) iterations... |
| Researcher Affiliation | Academia | Moran Feldman Department of Computer Science University of Haifa Haifa 3498838, Israel EMAIL Amin Karbasi School of Engineering and Applied Science Yale University New Haven, CT 06520 EMAIL |
| Pseudocode | Yes | Algorithm 1: COORDINATE-ASCENT (ε) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper does not conduct empirical studies, use datasets, or describe any training process. Therefore, there is no information about public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or dataset usage, so there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical algorithm design and analysis, and therefore does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs, hence no software dependencies with specific version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their approximation guarantees, but it does not detail any experimental setup, hyperparameters, or system-level training settings. |