Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
Authors: Shashank Singh, Barnabas Poczos
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires k as the sample size n ) into the functional of interest, the estimators we consider fix k and perform a bias correction. This is more efficient computationally, and, as we show in certain cases, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees. |
| Researcher Affiliation | Academia | Shashank Singh Statistics & Machine Learning Departments Carnegie Mellon University sss1@andrew.cmu.edu Barnabás Póczos Machine Learning Departments Carnegie Mellon University bapoczos@cs.cmu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to the ITE toolbox, which contains MATLAB code for the estimators being analyzed, but this is a third-party resource ([48] by Zoltán Szabó) and not code released by the authors specifically for the analytical framework described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its theoretical work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or hyperparameters. |