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].
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. |