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 [1].
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
Authors: Shashank Singh, Barnabas Poczos
NeurIPS 2016 | Venue PDF | 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 EMAIL Barnabás Póczos Machine Learning Departments Carnegie Mellon University EMAIL |
| 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. |