Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Rotting Infinitely Many-Armed Bandits
Authors: Jung-Hun Kim, Milan Vojnovic, Se-Young Yun
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 33,744 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,616 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |