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..
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 | Venue PDF | 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. |