Optimal Rates for Random Fourier Features
Authors: Bharath Sriperumbudur, Zoltan Szabo
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a detailed finite-sample theoretical analysis about the approximation quality of RFFs by (i) establishing optimal (in terms of the RFF dimension, and growing set size) performance guarantees in uniform norm, and (ii) presenting guarantees in Lr (1 r < ) norms. |
| Researcher Affiliation | Academia | Bharath K. Sriperumbudur Department of Statistics Pennsylvania State University University Park, PA 16802, USA bks18@psu.edu Zolt an Szab o Gatsby Unit, CSML, UCL Sainsbury Wellcome Centre, 25 Howland Street London W1T 4JG, UK zoltan.szabo@gatsby.ucl.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code availability for the methodology described. |
| Open Datasets | No | This paper is purely theoretical and does not involve experiments with datasets, thus no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments, so there is no mention of hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations, thus it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, so it does not contain specific experimental setup details such as hyperparameter values or training configurations. |