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 [1].
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]. |