High-dimensional (Group) Adversarial Training in Linear Regression
Authors: Yiling Xie, Xiaoming Huo
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we will run numerical experiments to observe the empirical performances of (group) adversarial training in high-dimensional linear regression. |
| Researcher Affiliation | Academia | Yiling Xie School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, Georgia, USA yxie350@gatech.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our numerical experiments are based on synthetic data. The code has been submitted in the Supplementary Materials. |
| Open Datasets | No | We consider the following models to generate synthetic data: The response variable Y is generated by the Gaussian linear model, as stated in Assumption 1.2. |
| Dataset Splits | No | The paper mentions sample sizes for synthetic data generation but does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | In terms of computation, we apply the dual formulations, i.e., problem (2) and problem (8), and solve these convex optimization problems using the CVXPY toolbox [8]. (No version number for CVXPY is specified). |
| Experiment Setup | Yes | The standard deviation of the error ϵ is chosen as 0.1. [...] the perturbation magnitude is chosen in the order of 1/ n in the adversarial training; the ratio of the perturbation magnitude and the perturbation weight is chosen in the order of 1/ n in the group adversarial training. For the constant, we selected 1 for simplicity and experimental convenience. For the group adversarial training, we divide the parameter equally into 125 groups of size 4 for Model 1 and 200 groups of size 3 for Model 2. The sample sizes are chosen {50, 100, 150, 200, 250, 300, 350, 400} for Model 1 and {50, 100, 150, 200, 250, 350, 450, 550} for Model 2. |