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..
Rotting Infinitely Many-Armed Bandits
Authors: Jung-Hun Kim, Milan Vojnovic, Se-Young Yun
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present results of numerical experiments for randomly generated problem instances of rotting infinitely manyarmed bandits. These results validate the insights derived from our theoretical results. |
| Researcher Affiliation | Academia | 1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea 2London School of Economics, London, UK. Correspondence to: Se-Young Yun <EMAIL>, Milan Vojnovi c <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 UCB-Threshold Policy (UCB-TP); Algorithm 2 Adaptive UCB-Threshold Policy (AUCB-TP) |
| Open Source Code | Yes | Our code is available at https://github.com/ junghunkim7786/rotting_infinite_armed_ bandits |
| Open Datasets | No | The paper states 'We generate initial mean rewards of arms by sampling from uniform distribution on [0, 1].' This indicates the use of synthetic data generation rather than a specific, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper uses synthetic data and does not mention explicit training, validation, or test dataset splits. The experiments are conducted over a 'time horizon T' rather than on pre-partitioned static datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that would be needed for replication. |
| Experiment Setup | Yes | We generate initial mean rewards of arms by sampling from uniform distribution on [0, 1]. In each time step, stochastic reward from pulling an arm has a Gaussian noise with mean zero and variance 1. We repeat each experiment 10 times and compute confidence intervals for confidence probability 0.95. |