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..
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
Authors: Jinyuan Jia, Xiaoyu Cao, Neil Zhenqiang Gong7961-7969
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on MNIST and CIFAR10. For instance, our method achieves a certified accuracy of 91.1% on MNIST when arbitrarily modifying, deleting, and/or inserting 100 training examples. |
| Researcher Affiliation | Academia | Jinyuan Jia, Xiaoyu Cao, Neil Zhenqiang Gong Duke University EMAIL |
| Pseudocode | Yes | Algorithm 1 CERTIFY Input: A, D, k, N, De, α. Output: Predicted label and certified poisoning size for each testing example. |
| Open Source Code | Yes | Code is available at: https://github.com/jjy1994/Bagging Certify Data Poisoning. |
| Open Datasets | Yes | We use MNIST and CIFAR10 datasets. The number of training examples in the two datasets are 60,000 and 50,000, respectively, which are the training datasets that we aim to certify. |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a distinct validation dataset split or how it was used in the experimental setup. |
| Hardware Specification | Yes | We performed experiments on a server with 80 CPUs@2.1GHz, 8 GPUs (RTX 6,000), and 385 GB main memory. |
| Software Dependencies | No | The paper mentions software like Keras and TensorFlow but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Our method has three parameters, i.e., k, α, and N. Unless otherwise mentioned, we adopt the following default settings for them: α = 0.001, N = 1,000, k = 30 for MNIST, and k = 500 for CIFAR10. |