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..
Budgeted Multi-Armed Bandits with Multiple Plays
Authors: Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu, Tie-Yan Liu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted a set of numerical simulations to test the empirical performance of our policy. |
| Researcher Affiliation | Collaboration | Yingce Xia1, Tao Qin2, Weidong Ma2, Nenghai Yu1 and Tie-Yan Liu2 1University of Science and Technology of China 2Microsoft Research Asia |
| Pseudocode | Yes | Algorithm 1: Mg for Known Distributions |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. It only provides a link to the full version of the paper itself. |
| Open Datasets | No | We simulated the bandit with two distributions: one with multinomial distribution, and the other with beta distribution. For each distribution, we simulated a 10-armed bandit and a 50-armed bandit. Detailed parameters of the distributions are left in Appendix H.1 due to limited space. |
| Dataset Splits | No | The paper describes simulating bandit problems and running policies, but it does not specify any training, validation, or test dataset splits in terms of percentages, sample counts, or predefined partitions. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | MRCB has a hyper parameter . We searched the in the set {2 10, 2 7, 2 4, 21} and found that = 2 4 worked well for most cases. Therefore, we fix 2 4 in the following experiments. |