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..
Nearest Neighbour with Bandit Feedback
Authors: Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give generic regret bounds for our algorithm and further analyse them when applied to the stochastic bandit problem in euclidean space. In Appendix E we prove, in order, all of the theorems stated in this paper. |
| Researcher Affiliation | Academia | Stephen Pasteris The Alan Turing Institute London UK EMAIL Chris Hicks The Alan Turing Institute London UK EMAIL Vasilios Mavroudis The Alan Turing Institute London UK EMAIL |
| Pseudocode | Yes | Algorithm 1 CANPROP at trial t |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical problem setting involving data, but does not mention the use or availability of a specific publicly accessible dataset for experimental purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper provides pseudocode for algorithms but does not specify any software dependencies with version numbers required for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments; therefore, no experimental setup details such as hyperparameters or training settings are provided. |