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..
An adaptive nearest neighbor rule for classification
Authors: Akshay Balsubramani, Sanjoy Dasgupta, yoav Freund, Shay Moran
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theory and experiments that demonstrate that the algorithm performs comparably to, and sometimes better than, k-NN with an optimal choice of k. We performed a few experiments using real-world data sets from computer vision and genomics (see Section C). |
| Researcher Affiliation | Academia | Akshay Balsubramani EMAIL Sanjoy Dasgupta EMAIL Yoav Freund EMAIL Shay Moran EMAIL |
| Pseudocode | Yes | Figure 2: The adaptive k-NN (AKNN) classifier. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement that its own source code is openly available. |
| Open Datasets | Yes | We performed a few experiments using real-world data sets from computer vision and genomics (see Section C). [MNI96] MNIST dataset. http://yann.lecun.com/exdb/mnist/, 1996. [not11] not MNIST dataset. http://yaroslavb.com/upload/not MNIST/, 2011. Accessed: 2019-05-02. [Mou18] Mouse cell atlas dataset. ftp://ngs.sanger.ac.uk/production/teichmann/ BBKNN/Mouse Atlas.zip, 2018. Accessed: 2019-05-02. |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not specify training, validation, or test splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | Parametrization: We replace Equation (6) with = A p k, where A is a confidence parameter corresponding to the theory s δ (given n). |