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..
Multi-player Multi-armed Bandits with Delayed Feedback
Authors: Jingqi Fan, Zilong Wang, Shuai Li, Linghe Kong
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and real-world datasets validate the effectiveness of our algorithm. |
| Researcher Affiliation | Academia | Jingqi Fan1 , Zilong Wang2 , Shuai Li2 , Linghe Kong2 1Northeastern University, China 2Shanghai Jiao Tong University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 DDSE (Leader with j = M) Algorithm 2 Communication (Leader with j = M) Algorithm 3 Communication (Follower j) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code for their methodology, nor does it include links to source code repositories. The only link provided is for a dataset. |
| Open Datasets | Yes | We evaluate our algorithms using real-world spectrum data collected in Finland by the 5G-Xcast project2. ... The full dataset used in this experiment is publicly available at https://zenodo.org/records/1293283. |
| Dataset Splits | No | The paper mentions running experiments for T = 300,000 rounds and averaging over 20 trials, and using real-world spectrum data. However, it does not specify any dataset splits like training/test/validation percentages or counts, or predefined splits for the real-world or synthetic data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Each experiment runs for T = 300,000 rounds and is averaged over 20 trials. Default parameters are K = 20, M = 10, E[d] = 200, σd = 100, and = 0.05. |