AdaLinUCB: Opportunistic Learning for Contextual Bandits
Authors: Xueying Guo, Xiaoxiao Wang, Xin Liu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 guoxueying@outlook.com, {xxwa, xinliu}@ucdavis.edu |
| 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. |