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..
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints
Authors: Shufan Wang, Guojun Xiong, Jian Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results further demonstrate the effectiveness of our Fair-UCRL. In this section, we first evaluate the performance of Fair-UCRL in simulated environments, and then demonstrate the utility of Fair-UCRL by evaluating it under three real-world applications of RMAB. |
| Researcher Affiliation | Academia | Stony Brook University EMAIL |
| Pseudocode | Yes | Algorithm 1: Fair-UCRL |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We study the PASCAL recognizing textual entailment task as in Snow et al. (2008). We study the continuous positive airway pressure therapy (CPAP) as in Herlihy et al. (2023); Li and Varakantham (2022b). We study the land mobile satellite system problem as in Prieto-Cerdeira et al. (2010). |
| Dataset Splits | No | The paper describes an online learning setting with episodes, where the DM estimates transition kernels and reward functions by observing trajectories. It does not provide traditional train/validation/test splits for a static dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The activation budget is set to 100. The minimum activation fraction η is set to be 0.1, 0.2 and 0.3 for the three classes of arms, respectively. We set K = H = 160. We use Monte Carlo simulations with 1, 000 independent trials. The budget is B = 5 and the fairness constraint is set to be a random number between [0.1, 0.7]. |