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-armed Bandits with Compensation
Authors: Siwei Wang, Longbo Huang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present experimental results to demonstrate the performance of the algorithms. |
| Researcher Affiliation | Academia | Siwei Wang IIIS, Tsinghua University EMAIL; Longbo Huang IIIS, Tsinghua University EMAIL |
| Pseudocode | Yes | Algorithm 1: The UCB algorithm for KCMAB. Algorithm 2: The modified "-greedy algorithm for KCMAB. Algorithm 3: The Modified Thompson Sampling Algorithm for KCMAB. Algorithm 4: Procedure Update |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a simulated environment with a specific reward vector and number of time steps, but does not refer to a publicly available dataset with concrete access information (link, DOI, citation). |
| Dataset Splits | No | The paper describes a simulated game run for 10000 time steps and averaged over 1000 runs, but does not specify train, validation, or test dataset splits in the conventional sense of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, there are a total of nine arms with expected reward vector µ = [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]. We run the game for T = 10000 time steps. [...] In our experiment, we choose = 20. [...] Here we choose to be 10,15 and 20. |