On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
Authors: Wei Cao, Jian Li, Yufei Tao, Zhize Li
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Due to the space constraint, we show only the experiments that compare ME-AS and AMCV [14] for k-MCV problem. Additional experiments can be found in Appendix G. We use two synthetic data sets and one real world data set to evaluate the algorithms. |
| Researcher Affiliation | Academia | 1Tsinghua University 2Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1: ME-AS |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes the synthetic and real-world datasets used (power law distribution graphs, Twitter user relationships) but does not provide specific links, DOIs, repository names, or citations to publicly available versions of these datasets or instructions on how to access them. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages for training, validation, and testing, or methods like cross-validation). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | We fix the parameters δ = 0.1, k = 20 and enumerate ϵ from 0.01 to 0.1. We use the same parameter ξ = 2000 for AMCV as in [14]. For ME-AS, we first take ξ = 107 for each round of the median elimination step and then we use the previous sample cost dividing 250 as the samples of the uniform sampling step. |