Efficient and Robust High-Dimensional Linear Contextual Bandits
Authors: Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of our algorithm and verify our theoretical guarantees. |
| Researcher Affiliation | Collaboration | Cheng Chen1 , Luo Luo2 , Weinan Zhang1 , Yong Yu1 and Yijiang Lian3 1Shanghai Jiao Tong University 2Hong Kong University of Science and Technology 3Baidu jack chen1990@sjtu.edu.cn, luoluo@ust.hk, {wnzhang, yyu}@apex.sjtu.edu.cn, lianyijiang@baidu.com |
| Pseudocode | Yes | Algorithm 1 SCFD and Algorithm 2 CBSCFD |
| Open Source Code | No | The paper states 'The code is implemented in Matlab R2017b.' but does not provide any links to its source code or explicitly state its public release. |
| Open Datasets | Yes | We perform our experiments on two real-world data sets: MNIST [Le Cun et al., 1998] and CIFAR10 [Krizhevsky and Hinton, 2009]. |
| Dataset Splits | No | The paper describes how data samples are drawn for online classification in a sequential manner for the contextual bandit problem, rather than specifying traditional train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | We conduct all experiments on a Linux server which contains 8 processors and has total memory of 32GB. (This lacks specific CPU/GPU models, only providing general system characteristics). |
| Software Dependencies | Yes | The code is implemented in Matlab R2017b. |
| Experiment Setup | Yes | The parameter β of all methods is searched in {10 4, 10 3, . . . , 1} and λ is searched in {2 10 4, 2 10 3, . . . , 2 104}. We choose the best values for each approach and report the average results in Figure 1. |