Towards a Unified Analysis of Random Fourier Features
Authors: Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report the results of our numerical experiments (on both simulated and real-world datasets) aimed at validating our theoretical results and demonstrating the utility of Algorithm 1. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Oxford, United Kingdom 2Department of Informatics, King s College London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 APPROXIMATE LEVERAGE WEIGHTED RFF |
| Open Source Code | No | The paper references third-party software like LIBSVM and scikit-learn that they used, but does not provide a link or statement about open-sourcing their own implementation code for the work described in the paper. |
| Open Datasets | Yes | We use four datasets from Chang & Lin (2011) and Dheeru & Karra Taniskidou (2017) for this purpose, including two for regression and two for classification: CPU, KINEMATICS, COD-RNA and COVTYPE. ... Dheeru, D. and Karra Taniskidou, E. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | The Gaussian/RBF kernel is used for all the datasets with hyper-parameter tuning via 5-fold inner cross validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using "the ridge regression and SVM package from Pedregosa et al. (2011)" (scikit-learn) and referring to "LIBSVM" (Chang & Lin, 2011), but it does not specify any version numbers for these software packages or other dependencies. |
| Experiment Setup | No | The paper mentions "hyper-parameter tuning via 5-fold inner cross validation" for the Gaussian/RBF kernel but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations. |