Online Consistency of the Nearest Neighbor Rule
Authors: Geelon So, Sanjoy Dasgupta
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 finite, upper doubling measure. |
| Researcher Affiliation | Academia | Sanjoy Dasgupta Department of Computer Science UC San Diego La Jolla, CA 92023 dasgupta@ucsd.edu; Geelon So Department of Computer Science UC San Diego La Jolla, CA 92023 geelon@ucsd.edu |
| 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 η( Xn) 4: Observe and memorize the ground-truth label η(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. |