Run-off Election: Improved Provable Defense against Data Poisoning Attacks
Authors: Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini, Soheil Feizi
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods on MNIST, CIFAR-10, and GTSRB and obtain improvements in certified accuracy by up to 3%-4%. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Maryland, MD, USA. |
| Pseudocode | Yes | The formal pseudocode of ROE is provided in Algorithm 1. |
| Open Source Code | Yes | Our code can be found in this github repository. |
| Open Datasets | Yes | We similarly use Network-In-Network (Lin et al., 2013) architecture, to be trained with the set of hyperparameters from (Gidaris et al., 2018). We similarly use Network-In-Network (Lin et al., 2013) architecture, to be trained with the set of hyperparameters from (Gidaris et al., 2018). |
| Dataset Splits | No | The paper mentions 'training data' and 'test samples' and states 'We consider the same setup as prior work (Levine & Feizi, 2020; Wang et al., 2022b)', implying standard splits. However, it does not explicitly describe a separate 'validation' split or its specific percentages/counts. |
| Hardware Specification | Yes | by using a single NVIDIA Ge Force RTX 2080 Ti GPU |
| Software Dependencies | No | The paper does not specify version numbers for any software or libraries used (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | We similarly use Network-In-Network (Lin et al., 2013) architecture, to be trained with the set of hyperparameters from (Gidaris et al., 2018). |