Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Precise Performance Analysis of Support Vector Regression
Authors: Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini
ICML 2021 | Venue PDF | 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 <EMAIL>. |
| 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 . |