Queueing Matching Bandits with Preference Feedback
Authors: Jung-hun Kim, Min-hwan Oh
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 junghunkim@snu.ac.kr Min-hwan Oh Seoul National University Seoul, South Korea minoh@snu.ac.kr |
| 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. |