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..
Efficient and Robust High-Dimensional Linear Contextual Bandits
Authors: Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian
IJCAI 2020 | Venue PDF | 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 EMAIL, EMAIL, EMAIL, EMAIL |
| 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. |