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..
On the Effects of Fairness to Adversarial Vulnerability
Authors: Cuong Tran, Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on non-linear models and different architectures validate the theoretical ๏ฌndings.This section empirically validates the theoretical insights discussed earlier, extending them to more complex architectures, datasets, and loss functions. |
| Researcher Affiliation | Academia | Cuong Tran1 , Keyu Zhu2 , Pascal Van Hentenryck2 and Ferdinando Fioretto1 1University of Virginia 2Georgia Institute of Technology |
| Pseudocode | No | The paper describes methods in prose but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | Datasets. The experiments of this section focus on three vision datasets: UTK-Face [Zhang et al., 2017], FMNIST [Xiao et al., 2017] and CIFAR-10 [Krizhevsky et al., 2009]. |
| Dataset Splits | No | The paper mentions using UTK-Face, FMNIST, and CIFAR-10 datasets and refers to 'standard labels' for some, but does not provide specific percentages or counts for train/validation/test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU or CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions specific software like 'Torchattacks' by reference but does not provide specific version numbers for software dependencies used in their own experimental setup. |
| Experiment Setup | Yes | Models trained on the UTK-Face data use a learning rate of 1e 3 and 70 epochs. Those trained on FMNIST and CIFAR, use a learning rate of 1e 1 and 200 epochs, as suggested in previous work [Xu et al., 2021a]. |