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..
Queueing Matching Bandits with Preference Feedback
Authors: Jung-hun Kim, Min-hwan Oh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we provide experimental results to demonstrate the performance of our algorithms. |
| Researcher Affiliation | Academia | Jung-hun Kim Seoul National University Seoul, South Korea EMAIL Min-hwan Oh Seoul National University Seoul, South Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 UCB-Queueing Matching Bandit (UCB-QMB) [...] Algorithm 2 Thompson Sampling-Queueing Matching Bandit (TS-QMB) |
| Open Source Code | Yes | The source code is available at https://github.com/junghunkim7786/Queueing-Matching-Bandits-with-Preference-Feedback |
| Open Datasets | Yes | For the synthetic experiments, we consider N = 4, K = 2, L = 2, and d = 2. Each element in xn and θk is uniformly generated from [0, 1] and then normalized, and λn s are determined with ϵ = 0.1. [...] The source code is available at https://github.com/junghunkim7786/Queueing-Matching-Bandits-with-Preference-Feedback |
| Dataset Splits | No | The paper describes parameters for generating synthetic data but does not explicitly mention any training, validation, or testing splits of this generated data. The experiments are conducted over a 'Time step t'. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. The NeurIPS checklist confirms this, stating, 'The conducted experiments do not require significant computing power.' |
| Software Dependencies | No | The paper does not list specific versions for any software dependencies (e.g., Python, PyTorch, TensorFlow, or any libraries/solvers). |
| Experiment Setup | Yes | For the synthetic experiments, we consider N = 4, K = 2, L = 2, and d = 2. Each element in xn and θk is uniformly generated from [0, 1] and then normalized, and λn s are determined with ϵ = 0.1. |