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..
A Dynamic Algorithm for Weighted Submodular Cover Problem
Authors: Kiarash Banihashem, Samira Goudarzi, Mohammadtaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We initiate the study of the submodular cover problem in dynamic setting where the elements of the ground set are inserted and deleted. ... For this problem, we propose a randomized algorithm that, in expectation, obtains a (1 O(ϵ), O(ϵ 1))-bicriteria approximation using polylogarithmic query complexity per update. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Maryland, MD, USA 2Department of Mathematics and Computer Science, TU Eindhoven, the Netherlands. Correspondence to: Samira Goudarzi <EMAIL>, Kiarash Banihashem <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Parallel Runs ... Algorithm 2 Data Structure Construction ... Algorithm 3 Insertion ... Algorithm 4 Deletion ... Algorithm 5 CALCSAMPLESIZE |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve empirical experiments with datasets for training, validation, or testing. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not involve empirical experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not involve empirical experiments, so no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not involve empirical experiments, so no experimental setup details like hyperparameters or training settings are provided. |