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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Online Consistency of the Nearest Neighbor Rule
Authors: Geelon So, Sanjoy Dasgupta
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,990 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,120 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove online consistency for all measurable functions in doubling metric spaces under the mild assumption that the instances are generated by a process that is uniformly absolutely continuous with respect to a ο¬nite, upper doubling measure. |
| Researcher Affiliation | Academia | Sanjoy Dasgupta Department of Computer Science UC San Diego La Jolla, CA 92023 EMAIL; Geelon So Department of Computer Science UC San Diego La Jolla, CA 92023 EMAIL |
| Pseudocode | Yes | Algorithm 1 The 1-nearest neighbor rule 1: for n = 1, 2, . . . do 2: Receive the instance Xn 3: Predict with a nearest neighbor label Ξ·( Xn) 4: Observe and memorize the ground-truth label Ξ·(Xn) 5: end for |
| Open Source Code | No | The paper is theoretical and does not mention providing any open-source code. |
| Open Datasets | No | This is a theoretical work and does not involve experiments or datasets. The NeurIPS Paper Checklist also indicates 'NA' for questions related to experimental results and data. |
| Dataset Splits | No | This is a theoretical work and does not involve experiments or datasets, therefore no training/test/validation splits are discussed. |
| Hardware Specification | No | This is a theoretical work and does not involve experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical work and does not involve experiments, thus no software dependencies are listed. |
| Experiment Setup | No | This is a theoretical work and does not involve experiments, thus no experimental setup details like hyperparameters or training settings are provided. |