Budgeted Multi-Armed Bandits with Multiple Plays
Authors: Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu, Tie-Yan Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |