Consistent Submodular Maximization
Authors: Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |