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..
High-dimensional (Group) Adversarial Training in Linear Regression
Authors: Yiling Xie, Xiaoming Huo
NeurIPS 2024 | Venue PDF | 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 EMAIL |
| 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. |