An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
Authors: Jung-hun Kim, Milan Vojnovic, Se-Young Yun
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we demonstrate the performance of our algorithm using numerical experiments. |
| Researcher Affiliation | Academia | Jung-hun Kim Seoul National University Seoul, South Korea junghunkim@snu.ac.kr Milan Vojnovic London School of Economics London, United Kingdom m.vojnovic@lse.ac.uk Se-Young Yun KAIST AI Seoul, South Korea yunseyoung@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 UCB-Threshold with Adaptive Sliding Window |
| Open Source Code | Yes | The source code is available at https://github.com/junghunkim7786/An-Adaptive-Approach-for-Infinitely-Many-armed-Bandits-under-Generalized-Rotting-Constraints |
| Open Datasets | No | We use randomly generated datasets under a uniform distribution for initial mean rewards (β = 1). The paper does not provide a link, DOI, or formal citation for this dataset as it is synthetically generated. |
| Dataset Splits | No | The paper mentions using 'randomly generated datasets' but does not specify any training, validation, or test split percentages or methodology. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, or specific libraries). |
| Experiment Setup | Yes | We use randomly generated datasets under a uniform distribution for initial mean rewards (β = 1). For comparison with UCB-TP, recall our discussion in Remark 3.2. We set the rotting rates such that ρt = 1/(t log(T)) for all t, for which ρ = ρ1 = 1/ log(T) = o(1), VT = O(1), and ST = T. |