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..
Adversarial Training and Provable Robustness: A Tale of Two Objectives
Authors: Jiameng Fan, Wenchao Li7367-7376
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform both theoretical analysis on the convergence of the proposed technique and experimental comparison with state-of-the-arts. Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l robustness. |
| Researcher Affiliation | Academia | Jiameng Fan , Wenchao Li Department of Electrical and Computer Engineering, Boston University, Boston EMAIL |
| Pseudocode | Yes | Algorithm 1 Weight Updates and Algorithm 2 Joint Training |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l robustness. |
| Dataset Splits | No | The paper mentions 'test dataset' and 'test examples' but does not provide specific training/validation/test dataset splits, percentages, or explicit sample counts for reproduction. |
| Hardware Specification | Yes | We perform all experiments on a desktop server using at most 4 Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Algorithm 2 Joint Training Input Warm-up epochs Tnat and Tadv, ϵtrain ramp-up epochs R, maximum FOSC value cmax... ct=clip(cmax (t R) cmax/T , 0, cmax)... κadv, κIBP, κreg=COMPUTE WEIGHTS(xadv, t, ct)... loss=κadv Ladv(θt)+κIBPLIBP(θt)+κreg LIBP(θt) 2 2... θt+1=θt ηtgfinal(θt) gfinal(θt): stochastic gradient |