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..
Fully Dynamic Submodular Maximization over Matroids
Authors: Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a randomized algorithm that maintains an efο¬cient data structure with an O(k2) amortized update time (in the number of additions and deletions) and yields a 4-approximate solution, where k is the rank of the matroid. |
| Researcher Affiliation | Collaboration | 1Google Research 2Department of Computer, Control and Management Engineering Antonio Ruberti , Sapienza University of Rome, Rome, Italy. |
| Pseudocode | Yes | Algorithm 1 SWAPPING; Algorithm 2 INITIALIZATION; Algorithm 3 INSERTION(e); Algorithm 4 DELETION(e); Algorithm 5 LEVEL-CONSTRUCT(β); Algorithm 6 THRESHOLD-SWAPPING; Algorithm 7 INSERTION(e); Algorithm 8 LEVEL-CONSTRUCT(β) |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical evaluation on datasets. Therefore, no datasets are mentioned as publicly available. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no training, validation, or test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical evaluation. No hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical evaluation. No software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical evaluation. No experimental setup details, such as hyperparameters or system-level training settings, are provided. |