Kernel quadrature with DPPs
Authors: Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally compare DPPs to existing kernel-based quadratures such as herding, Bayesian quadrature, or leverage score sampling. Numerical results confirm the interest of DPPs, and even suggest faster rates than our bounds in particular cases. 5 Numerical simulations |
| Researcher Affiliation | Academia | Ayoub Belhadji, Rémi Bardenet, Pierre Chainais Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRISt AL, Villeneuve d Ascq, France {ayoub.belhadji, remi.bardenet, pierre.chainais}@univ-lille.fr |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper defines mathematical spaces and kernels (e.g., 'uniform measure on X = [0, 1]', 'Gaussian measure on X = R') for its numerical simulations, but does not use or provide access information for a distinct publicly available or open dataset. |
| Dataset Splits | No | The paper performs numerical simulations based on mathematical constructs rather than using empirical datasets with explicit train/validation/test splits. Therefore, no specific dataset split information for validation is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | We take N [5, 50]. Figures 1a and 1b show log-log plots of the worst case quadrature error w.r.t. N, averaged over 50 samples for each point, for s {1, 3}. We take N [5, 1000] and s = 1. regularization parameter λ {0, 0.1, 0.2} |