Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Authors: Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also include experiments that demonstrate the improved performance of our algorithms compared with benchmark algorithms on synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong, Hong Kong SAR, China 2University of Massachusetts Amherst, MA, United States 3California Institute of Technology, CA, United States 4Microsoft Research, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 C2-UCB-T: Contextual Combinatorial Upper Confidence Bound Algorithm for C2MAB-T |
| Open Source Code | No | No explicit statement about the release of their own source code or a link to a code repository was found. |
| Open Datasets | Yes | Movie Lens-1M dataset grouplens.org/datasets/movielens/1m/ |
| Dataset Splits | No | No explicit mention of specific train/validation/test dataset splits (e.g., percentages or sample counts) was found. The paper describes data usage but not data partitioning for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper (e.g., programming languages or libraries like Python, PyTorch, or scikit-learn). |
| Experiment Setup | Yes | We set d = 20, K = 4, and the goal is to choose K out of m movies to maximize the reward of the cascading recommendation. We use their learned feature mapping ϕ from movies to the probability that a uniformly random user rated the movie more than three stars. |