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..
On the Intrinsic Differential Privacy of Bagging
Authors: Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate Bagging on MNIST and CIFAR10. Our experimental results demonstrate that Bagging achieves significantly higher accuracies than state-of-the-art differentially private machine learning methods with the same privacy budgets. |
| Researcher Affiliation | Academia | Hongbin Liu , Jinyuan Jia and Neil Zhenqiang Gong Duke University EMAIL |
| Pseudocode | No | The paper describes the algorithms conceptually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using 'open-source implementations of DPSGD and PATE from their authors' but does not provide a statement or link indicating that the code for their Bagging method is open-source. |
| Open Datasets | Yes | We adopt MNIST [Le Cun et al., 2010] and CIFAR10 [Krizhevsky et al., 2009] as the sensitive training datasets... When MNIST is the sensitive training dataset, we adopt Fashion-MNIST [Xiao et al., 2017] as the public non-sensitive dataset... When CIFAR10 is the sensitive training dataset, we assume Image Net [Deng et al., 2009] is the public non-sensitive dataset. |
| Dataset Splits | No | The paper mentions training and testing on datasets like MNIST and CIFAR10, but it does not explicitly specify details for a validation dataset split or how it was used for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using open-source implementations of other methods and VGG16, but it does not specify version numbers for key software components or libraries (e.g., Python, deep learning frameworks). |
| Experiment Setup | Yes | We set training epochs=100 for both Bagging and DPSGD in both Case I and Case II. For PATE [Papernot et al., 2018], we set the number of teachers to be 250, and both teacher and student models are trained for 1,000 epochs... Following the authors of PATE, we set T= 200, σ1 = 150, and σ2 = 40... we replace the last fully connected layer of a pretrained model as a new one that has the same number of classes as the sensitive training dataset. We then fine-tune the model using a learning rate that is 10 times smaller than that when training from scratch. |