Optimal rates for k-NN density and mode estimation
Authors: Sanjoy Dasgupta, Samory Kpotufe
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present two related contributions of independent interest: (1) high-probability finite sample rates for k-NN density estimation, and (2) practical mode estimators based on k-NN which attain minimax-optimal rates under surprisingly general distributional conditions. The proof of these results are concise applications of Lemma 2 above. They are given in the appendix (long version). |
| Researcher Affiliation | Academia | Sanjoy Dasgupta University of California, San Diego, CSE dasgupta@eng.ucsd.edu Samory Kpotufe Princeton University, ORFE samory@princeton.edu |
| Pseudocode | Yes | Figure 1: Estimate the mode of a unimodal density f from X[n]. (Content: Return arg maxx X[n] fk(x).) Figure 3: Estimate the modes of a multimodal f from X[n]. The parameter ϵ serves to prune. (Content describing algorithm steps) |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not report on empirical experiments with datasets, and therefore does not mention public dataset availability. |
| Dataset Splits | No | This is a theoretical paper that does not report on empirical experiments with datasets, and therefore does not provide dataset split information. |
| Hardware Specification | No | This is a theoretical paper and does not specify hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training configurations. |