Generalization Properties of Learning with Random Features
Authors: Alessandro Rudi, Lorenzo Rosasco
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical findings are complemented by numerical simulation validating the bounds. ... In Figure 3, the theoretical and estimated behavior of the excess risk, λ and M with respect to n are reported together with their standard deviation over 100 repetitions. The experiment shows that the predictions by Thm. 3 are accurate, since the theoretical predictions estimations are within one standard deviation from the values measured in the simulation. |
| Researcher Affiliation | Academia | Alessandro Rudi INRIA Sierra Project-team, Ecole Normale Sup erieure, Paris, 75012 Paris, France alessandro.rudi@inria.fr Lorenzo Rosasco University of Genova, Istituto Italiano di Tecnologia, Massachusetts Institute of Technology. lrosasco@mit.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled "Pseudocode" or "Algorithm", nor does it present any structured algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the methodology described, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper constructs a synthetic dataset based on a "spline kernel of order q" and a uniform distribution on [0, 1]. It does not use a publicly available dataset with concrete access information (link, DOI, or specific citation to a repository). |
| Dataset Splits | No | The paper describes a numerical simulation where the KRR estimator is computed, and then the RF-KRR estimator selects the number of features M to obtain an excess risk within a certain percentage of KRR. It does not specify traditional train/validation/test dataset splits (e.g., 80/10/10 percentages or specific sample counts) that are needed for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | In Figure 3, the theoretical and estimated behavior of the excess risk, λ and M with respect to n are reported together with their standard deviation over 100 repetitions. Parameters r = 11/16, γ = 1/8 (top), and r = 7/8, γ = 1/4 (bottom). |