Combinatorial Stochastic-Greedy Bandit
Authors: Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini, Vaneet Aggarwal
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as k grows. |
| Researcher Affiliation | Academia | King Abdullah University of Science and Technology (KAUST) Iowa State University Purdue University |
| Pseudocode | Yes | Algorithm 1: SGB |
| Open Source Code | No | The paper refers to an arXiv preprint (Fourati et al. 2023c) for the appendix, but it does not state that source code for the methodology is provided at this link or elsewhere. |
| Open Datasets | Yes | We experimented using a portion of the Facebook network (Leskovec and Mcauley 2012). |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, or test dataset splits (e.g., percentages, sample counts, or specific predefined splits). It mentions "we tested each method ten times" but not how the data was split for these tests. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions). |
| Experiment Setup | Yes | We used 0.1 uniform infection probabilities for each edge. For every time horizon T {2 10^4, 3 10^4, 4 10^4, 5 10^4}, we tested each method ten times. The curves for all methods are smoothed using a moving average with a window size of 100. Figures (1d), (1e), and (1f) illustrate immediate rewards over a horizon T = 5 10^4 for cardinality constraints k of 8, 24, and 32, and ϵ values around 0.251, 0.522, and 0.632, respectively. |