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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variable Kernel Density Estimation in High-Dimensional Feature Spaces
Authors: Christiaan van der Walt, Etienne Barnard
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of this estimator to state-of-the art maximumlikelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of highdimensional datasets considered. |
| Researcher Affiliation | Academia | Christiaan M. van der Walt a,b Etienne Barnard b a Modelling and Digital Science, CSIR, Pretoria, South Africa EMAIL b Multilingual Speech Technologies Group, North-West University, Vanderbijlpark, South Africa EMAIL |
| Pseudocode | No | The paper contains mathematical derivations and equations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it contain explicit code release statements or links. |
| Open Datasets | Yes | We select five RW datasets (with independent train and test sets) from the UCI Machine Learning Repository (Lichman 2013) for the purpose of simulation studies. |
| Dataset Splits | Yes | We therefore perform 10-fold cross-validation on each class specific training set to find the optimal number of training iterations for each class conditional density function. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The optimal kernel bandwidth is obtained with a direct approach where the right-hand side of each bandwidth estimation equation is initialised with the Silverman bandwidth, the left hand side is updated, and the updated bandwidth is then substituted into the right-hand side again. This process is repeated for 10 iterations on each training set. |