Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
Authors: James Jordon, Jinsung Yoon, Mihaela van der Schaar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the improvements our model makes over standard subsample-and-aggregate in two datasets (Heart Failure (private) and UCI Adult (public)). |
| Researcher Affiliation | Academia | James Jordon University of Oxford james.jordon@wolfson.ox.ac.uk Jinsung Yoon University of California, Los Angeles jsyoon0823@g.ucla.edu Mihaela van der Schaar University of Cambridge University of California, Los Angeles Alan Turing Institute mv472@cam.ac.uk, mihaela@ee.ucla.edu |
| Pseudocode | Yes | Algorithm 1 Semi-supervised differentially private knowledge transfer using multiple partitions |
| Open Source Code | Yes | Implementation of DPBag can be found at https://bitbucket.org/mvdschaar/mlforhealthlabpub/src/master/alg/dpbag/. |
| Open Datasets | Yes | We demonstrate the improvements our model makes over standard subsample-and-aggregate in two datasets (Heart Failure (private) and UCI Adult (public)). |
| Dataset Splits | Yes | We randomly divide the data into 3 disjoint subsets: (1) a training set (33%), (2) public data (33%), (3) a testing set (33%). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'logistic regression' and 'Gradient Boosting Method (GBM)' as models but does not specify software names with version numbers for implementation or dependencies. |
| Experiment Setup | Yes | We set δ = 10 5. We vary ϵ {1, 3, 5}, n {50, 100, 250} and k {10, 50, 100}. In all cases we set λ = 2 n. |