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..
Quadrature-based features for kernel approximation
Authors: Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive the convergence behaviour and conduct an extensive empirical study that supports our hypothesis |
| Researcher Affiliation | Academia | Skolkovo Institute of Science and Technology Moscow, Russia Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow, Russia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The code for this paper is available at https://github.com/maremun/quffka. |
| Open Datasets | Yes | We extensively study the proposed method on several established benchmarking datasets: Powerplant, LETTER, USPS, MNIST, CIFAR100 [23], LEUKEMIA [20]. |
| Dataset Splits | No | The paper provides dataset names and overall sizes in Table 2, but it does not specify explicit train/validation/test splits (e.g., percentages, sample counts, or references to predefined splits) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) 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 | Approximation was constructed for different number of SR samples n = D 2(d+1)+1, where d is an original feature space dimensionality and D is the new one. For the Gaussian kernel we set hyperparameter γ = 1 2σ2 to the default value of 1 d for all the approximants. |