Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Consistent Submodular Maximization
Authors: Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 20,728 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,346 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 in๏ฌuence 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 ฮต = 0.1 in SIEVE-STREAMING and CHASING-LOCAL-OPT, while the cardinality constraint k is consistently set to 20. |