An analysis of Ermakov-Zolotukhin quadrature using kernels
Authors: Ayoub Belhadji
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 ayoub.belhadji@ens-lyon.fr |
| 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}. |