An adaptive nearest neighbor rule for classification
Authors: Akshay Balsubramani, Sanjoy Dasgupta, yoav Freund, Shay Moran
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 abalsubr@stanford.edu Sanjoy Dasgupta dasgupta@eng.ucsd.edu Yoav Freund yfreund@eng.ucsd.edu Shay Moran shaym@princeton.edu |
| 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). |