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..
Consistent Submodular Maximization
Authors: Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. |
| Researcher Affiliation | Collaboration | 1Google Research 2Sapienza University of Rome, Rome, Italy. |
| Pseudocode | Yes | Algorithm 1 ENCOMPASSING-SET |
| Open Source Code | Yes | The code of the experiments is available at https://github.com/fedefusco/Consistent-Submodular. |
| Open Datasets | Yes | We use the Facebook dataset from Mc Auley & Leskovec (2012) that consists of 4039 nodes V and 88234 edges E and, as measure of influence we consider the monotone and submodular dominating function: f(S) = |{v V : s S and (s, v) E}|. (...) We use the Run In Rome dataset (Fusco, 2022), that contains 8425 positions recorded by running activity in Rome, Italy. (...) We use the Movie Lens 1M database (Harper & Konstan, 2016), that contains 1000209 ratings for 3900 movies by 6040 users. |
| Dataset Splits | No | The paper discusses evaluation on real-world datasets but does not specify train/validation/test splits for reproducibility. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In our experiments, we set ε = 0.1 in SIEVE-STREAMING and CHASING-LOCAL-OPT, while the cardinality constraint k is consistently set to 20. |