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..
A Bayesian Approach for Subset Selection in Contextual Bandits
Authors: Jialian Li, Chao Du, Jun Zhu8384-8391
AAAI 2021 | Venue PDF | 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. |