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..
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Authors: Huimin Wu, Zhengmian Hu, Bin Gu10329-10337
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results show that our adversarial training algorithm enjoys robustness against various attacks and meanwhile has the similar efficiency and scalability with classical DSG algorithm. |
| Researcher Affiliation | Collaboration | Huimin Wu1, Zhengmian Hu2, Bin Gu1,3,4 1 School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China 2Department of Electrical & Computer Engineering, University of Pittsburgh, PA, USA 3JD Finance America Corporation, Mountain View, CA, USA 4MBZUAI, United Arab Emirates |
| Pseudocode | Yes | Algorithm 1 {αi}t i=1 = Train(P(x, y)) |
| Open Source Code | No | The DSG code is available at https://github.com/zixu1986/Doubly Stochastic Gradients. (This link is for the DSG framework on which their model is based, not their specific adv-SVM implementation code.) |
| Open Datasets | Yes | Datasets. We evaluate the robustness of adv-SVM on two well-known datasets, MNIST (Lecun and Bottou 1998) and CIFAR10 (Krizhevsky and Hinton 2009). |
| Dataset Splits | Yes | 5-fold cross validation is used to choose the optimal hyper-parameters (the regularization parameter C and the step size γ). |
| Hardware Specification | Yes | We perform experiments on Intel Xeon E5-2696 machine with 48GB RAM. |
| Software Dependencies | No | This algorithm is implemented in CVX a package for specifying and solving convex programs (Grant and Boyd 2014). (While CVX is mentioned, no specific version number is provided for CVX or any other software dependency.) |
| Experiment Setup | Yes | For FGSM and PGD, the maximum perturbation ϵ is set as 8/255 and the step size for PGD is ϵ/4. ... For ZOO, we use the ZOO-ADAM algorithm and set the step size η = 0.01, ADAM parameters β1 = 0.9, β2 = 0.999. ... the number of random features is set as 210 and the batch size is 500. 5-fold cross validation is used to choose the optimal hyper-parameters (the regularization parameter C and the step size γ). The parameters C and γ are searched in the region {(C, γ)| 3 log2 C 3 , 3 log2 γ 3}. |