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..
An analysis of Ermakov-Zolotukhin quadrature using kernels
Authors: Ayoub Belhadji
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the theoretical results by numerical experiments in Section 5.In this section, we illustrate the theoretical results presented in Section 3 in the case of the RKHS associated to the kernel ... Figure 1 shows log-log plots of the squared error w.r.t. N, averaged over 1000 samples for each point, for s {2, 3}. |
| Researcher Affiliation | Academia | Ayoub Belhadji Univ Lyon, ENS de Lyon Inria, CNRS, UCBL LIP UMR 5668, Lyon, France EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The numerical experiments are conducted in a theoretical setting (RKHS associated to a kernel, uniform measure on [0,1]), not on a publicly available dataset in the typical sense of machine learning datasets. The citation [5] refers to a textbook, not a specific dataset. |
| Dataset Splits | No | The paper does not describe traditional dataset splits (e.g., train/validation/test) as the experiments are numerical simulations based on theoretical frameworks rather than empirical evaluation on a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We take N [5, 100]. Figure 1 shows log-log plots of the squared error w.r.t. N, averaged over 1000 samples for each point, for s {2, 3}. and for KBIQ, it mentions M = 2N and γ = σ, for x that follows the distribution of the projection DPP and for g {e1, e10, e20}. |