A Precise Performance Analysis of Support Vector Regression
Authors: Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Particularly, we show numerically that the double descent behavior appears only when the H-SVR or S-SVR parameters are not properly tuned. Such a behavior reminds the recent findings in (Nakkiran et al., 2020b) that suggest that unregularized models often suffer from the sample-wise double descent phenomenon, while optimally tuned models usually present a monotonic risk with respect to the number of samples. |
| Researcher Affiliation | Academia | 1Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Correspondence to: Houssem Sifaou <houssem.sifaou@kaust.edu.sa>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data ('standard normal distribution') and does not provide concrete access information for a public dataset. |
| Dataset Splits | No | The paper does not mention using a validation set, nor does it specify any train/validation/test splits or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In a first experiment, we investigate the behavior of the test risk of H-SVR as a function of the number of samples for different values of the signal power β2 = β 2. Particularly, for each β {0.5, 1, 2}, we fix the noise variance σ2 and ϵ and plot the test risk and cosine similarity over the range [0, δ ] over which the H-SVR is feasible. Fig. 2 represents the theoretical results along with their empirical averages obtained for p = 200 and n = δp . |