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..
Run-off Election: Improved Provable Defense against Data Poisoning Attacks
Authors: Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini, Soheil Feizi
ICML 2023 | Venue PDF | 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). |