A Bayesian Approach for Subset Selection in Contextual Bandits
Authors: Jialian Li, Chao Du, Jun Zhu8384-8391
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show the effectiveness of our method on both linear bandits and general CB. Specifically, BRE outperforms previous methods like Bayes Gap and Lin Gap E on LB. We evaluate the performance of BRE by comparing with various baselines. Following (Xu, Honda, and Sugiyama 2018), we design experiments on synthetic data and a real-data simulated Explore-K problem. We test BRE on LB and some other general CB problems. |
| Researcher Affiliation | Collaboration | Jialian Li1, Chao Du2, Jun Zhu*1 1Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Lab, Bosch-Tsinghua Joint ML Center, Tsinghua University 2Alibaba Group |
| Pseudocode | Yes | Algorithm 1 Bayes Re-sample Explore |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We test the subset selection problems on Yahoo! Webscope Dataset R6A (Chu et al. 2009). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits (e.g., specific percentages, sample counts, or citations to predefined splits) for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper does not provide specific version numbers for any key software components or libraries (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | For all experiments, we set δ = 0.05, ε = 0.0 and λ = 1. |