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..
Infinitely Many-Armed Bandits with Budget Constraints
Authors: Haifang Li, Yingce Xia
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Theoretical analysis shows that this simple algorithm enjoys a sub-linear regret in term of the budget B. We also provide a lower bound of any algorithm under Bernoulli setting. The regret bound of RCB-I matches the lower bound up to a logarithmic factor. We further extend this algorithm to the any-budget setting (i.e., the budget is unknown in advance) and conduct corresponding theoretical analysis. |
| Researcher Affiliation | Academia | Haifang Li Institute of Automation, Chinese Academy of Sciences EMAIL Yingce Xia University of Science and Technology of China EMAIL |
| Pseudocode | Yes | Algorithm 1: RCB subroutine |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | No | The paper does not mention the use of any datasets for training or evaluation, as it is a theoretical work. |
| Dataset Splits | No | The paper is theoretical and does not provide information about dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |