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..
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
Authors: Jung-hun Kim, Milan Vojnovic, Se-Young Yun
NeurIPS 2024 | Venue PDF | 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 EMAIL Milan Vojnovic London School of Economics London, United Kingdom EMAIL Se-Young Yun KAIST AI Seoul, South Korea EMAIL |
| 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. |