Nearest Neighbour with Bandit Feedback
Authors: Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 spasteris@turing.ac.uk Chris Hicks The Alan Turing Institute London UK c.hicks@turing.ac.uk Vasilios Mavroudis The Alan Turing Institute London UK vmavroudis@turing.ac.uk |
| 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. |