Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Rates of Convergence for Nearest Neighbor Classification
Authors: Kamalika Chaudhuri, Sanjoy Dasgupta
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 13,558 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,131 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 analyze the behavior of nearest neighbor classification in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. We illustrate our upper and lower bounds by introducing a new smoothness class customized for nearest neighbor classification. |
| Researcher Affiliation | Academia | Kamalika Chaudhuri Computer Science and Engineering University of California, San Diego EMAIL Sanjoy Dasgupta Computer Science and Engineering University of California, San Diego EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets, thus no information on public availability. |
| Dataset Splits | No | The paper is theoretical and does not describe any specific dataset split information (e.g., train/validation/test percentages or counts). |
| Hardware Specification | No | The paper is theoretical and does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide any specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details like hyperparameter values or training configurations. |