Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal rates for k-NN density and mode estimation
Authors: Sanjoy Dasgupta, Samory Kpotufe
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 14,896 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,617 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present two related contributions of independent interest: (1) high-probability ο¬nite 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 EMAIL Samory Kpotufe Princeton University, ORFE EMAIL |
| 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 Ο΅ 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. |