Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
Authors: Jack E. Jewson, Sahra Ghalebikesabi, Chris C Holmes
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive empirical evidence by reporting the performance of our model and four relevant baselines on ten different data sets, for two different tasks; additionally analysing their sensitivity in the number of samples and the available privacy budget. |
| Researcher Affiliation | Academia | Jack Jewson Department of Economics and Business Universitat Pompeu Fabra Barcelona, Spain jack.jewson@upf.edu Sahra Ghalebikesabi Department of Statistics University of Oxford Oxford, UK sahra.ghalebikesabi@univ.ox.ac.uk Chris Holmes The Alan Turing Institute Department of Statistics University of Oxford Oxford, UK chris.holmes@stats.ox.ac.uk |
| Pseudocode | No | The paper refers to algorithms from other works (e.g., 'Algorithm 1; [83]'), but does not include any pseudocode or algorithm blocks within its own content. |
| Open Source Code | Yes | Our code can be found at https://github.com/sghalebikesabi/beta-bayes-ops. |
| Open Datasets | Yes | The evaluations are conducted on simulated and UCI [23] data sets. For the latter, we have included the two classification data sets that were previously analysed in other applications of OPS (adult and abalone) [66, 81], in addition to other popular UCI data sets. |
| Dataset Splits | No | The paper states that 'validation splits' were used for hyperparameter tuning, but does not provide specific percentages or methodology for the training/validation split, only that test splits constitute 10% of the original data. |
| Hardware Specification | Yes | While the final experimental results can be run within approximately two hours on a single Intel(R) Xeon(R) Gold 5118 CPU @ 2.30GHz core, the complete compute needed for the final results, debugging runs, and sweeps amounts to around 11 days. |
| Software Dependencies | No | The paper mentions software like 'stan probabilistic programming language [16]' and 'sklearn', but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | DPSGD is run for 14 + ϵ epochs, with clipping norm 1, batch size 100, and learning rate of 10 2. |