Minimum Robust Multi-Submodular Cover for Fairness
Authors: Lan N. Nguyen, My T. Thai9109-9116
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further investigate our algorithms performance in two applications of MINRF, Information Propagation for Multiple Groups and Movie Recommendation for Multiple Users. Our algorithms have shown to outperform baseline heuristics in both solution quality and the number of queries in most cases. |
| Researcher Affiliation | Academia | Lan N. Nguyen My T. Thai Department of Computer and Information Science and Engineering University of Florida, Gainesville, Florida 32611 |
| Pseudocode | Yes | Algorithm 1 RANDGR |
| Open Source Code | Yes | The source code is available at https://github.com/lannn2410/minrf. |
| Open Datasets | Yes | We use Facebook dataset from SNAP database (Leskovec and Krevl 2014), an undirected graph with 4,039 nodes and 88,234 edges." and "We use Movie Lens dataset from Group Lens (2015) database, which includes information of 10,381 movies; and their 20,000,264 ratings (ranging in [0, 5]) from 138,493 users. |
| Dataset Splits | No | The paper describes the datasets used (Facebook, Movie Lens) and some processing details (e.g., random user selection, graph samples for estimation) but does not provide explicit training, validation, or test dataset splits (e.g., percentages, counts, or cross-validation details). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud computing instance types used for its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as programming language versions or library versions (e.g., Python 3.8, PyTorch 1.9), used for the experiments. |
| Experiment Setup | Yes | We set α = 0.1. Results are averaged over 10 repetitions." and "With r = 0, we compare RANDGR, GREEDY and THRESGR (γ = 0.2) with SEP algorithm: which considers each constraint separately, runs greedy to find a set Si that fi(Si) 1 α and return i [m]Si. |