Implicit Regularization in Over-Parameterized Support Vector Machine
Authors: Yang Sui, Xin HE, Yang Bai
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we verify our theoretical findings through a variety of numerical experiments and compare the proposed method with explicit regularization. Our results illustrate the advantages of employing implicit regularization via gradient descent in conjunction with over-parameterization in sparse SVMs.4 Numerical Study |
| Researcher Affiliation | Academia | Yang Sui Xin He Yang Bai School of Statistics and Management Shanghai University of Finance and Economics suiyang1027@stu.sufe.edu.cn;he.xin17@mail.shufe.edu.cn; statbyang@mail.shufe.edu.cn |
| Pseudocode | Yes | Algorithm 1: Gradient Descent Algorithm for High-Dimensional Sparse SVM. |
| Open Source Code | No | The paper does not contain any statement about making the source code publicly available or providing a link to a code repository. |
| Open Datasets | No | In our simulations, unless otherwise specified, we follow a default setup. We generate 3n independent observations, divided equally for training, validation, and testing. The true parameters β is set to m1S with a constant m. Each entry of x is sampled as i.i.d. zero-mean Gaussian random variable, and the labels y are determined by a binomial distribution with probability p = 1/(1 + exp(x T β )). |
| Dataset Splits | Yes | We generate 3n independent observations, divided equally for training, validation, and testing. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as 'Python 3.8' or 'PyTorch 1.9'. |
| Experiment Setup | Yes | Default parameters are: true signal strength m = 10, number of signals s = 4, sample size n = 200, dimension p = 400, step size η = 0.5, smoothness parameter γ = 10 4, and initialization size α = 10 8. |