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 [1].
Training for Stable Explanation for Free
Authors: Chao Chen, Chenghua Guo, Rufeng Chen, Guixiang Ma, Ming Zeng, Xiangwen Liao, Xi Zhang, Sihong Xie
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China 2Key Laboratory of Trustworthy Distributed Computing and Service (Mo E), Beijing University of Posts and Telecommunications, China 3Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), China 4 Intel, USA 5Carnegie Mellon University, USA 6College of Computer and Data Science, Fuzhou University, China |
| Pseudocode | Yes | Algorithm 1 Attacking a pair of features |
| Open Source Code | Yes | The code can be found at https://github.com/ccha005/R2ET. |
| Open Datasets | Yes | We adopt DNNs for three tabular datasets: Bank [53], Adult, and COMPAS [54], and two image datasets, CIFAR-10 [36] and ROCT [22]... dual-input Siamese networks [48] are adopted on MNIST [40] and two graph datasets, BP [48] and ADHD [49]. |
| Dataset Splits | Yes | We divide each tabular dataset into training, validation, and test portions at a ratio of 70 : 15 : 15, respectively. |
| Hardware Specification | Yes | Both come with a 16-core Intel Xeon processor and four TITAN X GPUs. One installs 16.04.3 Ubuntu with 3.8.8 Python and 1.7.1 Py Torch, and the other installs 18.04.6 Ubuntu with 3.7.6 Python and 1.8.1 Py Torch. The MNIST and graph datasets are run on a machine with two 10-core Intel Xeon processors and five Ge Force RTX 2080 Ti GPUs, which installs 18.04.3 Ubuntu with 3.9.5 Python and 1.9.1 Py Torch. |
| Software Dependencies | Yes | One installs 16.04.3 Ubuntu with 3.8.8 Python and 1.7.1 Py Torch, and the other installs 18.04.6 Ubuntu with 3.7.6 Python and 1.8.1 Py Torch. The MNIST and graph datasets are run on a machine with two 10-core Intel Xeon processors and five Ge Force RTX 2080 Ti GPUs, which installs 18.04.3 Ubuntu with 3.9.5 Python and 1.9.1 Py Torch. |
| Experiment Setup | Yes | Table 4: (Hyper)-parameters used in experiments. SN means Siamese Networks for dual input. |