Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
AdaLinUCB: Opportunistic Learning for Contextual Bandits
Authors: Xueying Guo, Xiaoxiao Wang, Xin Liu
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,263 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,340 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, based on both synthetic and real-world dataset, we show that Ada Lin UCB significantly outperforms other contextual bandit algorithms, under large exploration cost fluctuations. |
| Researcher Affiliation | Academia | Xueying Guo , Xiaoxiao Wang and Xin Liu University of California, Davis EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Ada Lin UCB |
| Open Source Code | Yes | 1The supplementary material of this paper is available at: https: //github.com/xiaoxiao01/IJCAI19/blob/master/Supplementary.pdf |
| Open Datasets | Yes | We also test the performance of the algorithms using the data from Yahoo! Today Module. This dataset contains over 4 million user visits to the Today module in a ten-day period in May 2009 [Li et al., 2010]. For the variation factor, we use a real trace the sales of a popular store. It includes everyday turnover in two years [Rossman, 2015]. |
| Dataset Splits | No | The paper mentions using a dataset but does not specify exact train/validation/test split percentages, sample counts, or explicitly reference predefined splits that would allow reproduction of data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | In all the algorithms, we set α = 1.5 to make a fair comparison. |