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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Uniform Convergence Rates for Kernel Density Estimation
Authors: Heinrich Jiang
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,661 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,653 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 (1) derive ο¬nite-sample high-probability density estimation bounds for multivariate KDE under mild density assumptions which hold uniformly in x Rd and bandwidth matrices. We apply these results to (2) mode, (3) density level set, and (4) class probability estimation and attain optimal rates up to logarithmic factors. We then (5) provide an extension of our results under the manifold hypothesis. Finally, we (6) give uniform convergence results for local intrinsic dimension estimation. |
| Researcher Affiliation | Industry | 1Google. Correspondence to: Heinrich Jiang <EMAIL>. |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the methodology is available. |
| Open Datasets | No | The paper is theoretical and does not use specific publicly available datasets for training or empirical evaluation. It refers to 'samples drawn from f' but no actual dataset names or access information. |
| Dataset Splits | No | The paper is theoretical and does not mention training/validation/test dataset splits, as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used, as it does not conduct empirical experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or system-level training settings. |