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 Differential Privacy of Bayesian Inference
Authors: Zuhe Zhang, Benjamin Rubinstein, Christos Dimitrakakis
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Worked examples and experiments with Bayesian naïve Bayes and Bayesian linear regression illustrate the application of our mechanisms. ... Experiments Having proposed a number of mechanisms for approximating exact Bayesian inference in the general framework of probabilistic graphical models, we now demonstrate our approaches on two simple, well-known PGMs: the (generative) naïve Bayes classifier, and (discriminative) linear regression. |
| Researcher Affiliation | Academia | School of Mathematics and Statistics, Department of Computing and Information Systems, The University of Melbourne, Australia EMAIL, EMAIL Christos Dimitrakakis Univ-Lille-3, France Chalmers University of Technology, Sweden EMAIL |
| Pseudocode | Yes | Algorithm 1 Laplace Mechanism on Posterior Updates ... Algorithm 2 Laplace Mechanism in the Fourier Domain ... Algorithm 3 Mechanism for MAP Point Estimates |
| Open Source Code | No | No explicit statement or link providing access to the paper's source code. |
| Open Datasets | Yes | U.S. census records dataset from the Integrated Public Use Microdata Series (Minnesota Population Center 2009) with 370k records and 14 demographic features. ... https: //international.ipums.org accessed 2015-08-30. |
| Dataset Splits | No | The paper describes train and test splits, but no explicit mention of a separate validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers) are mentioned. |
| Experiment Setup | Yes | We trained our mechanisms on only 50 examples, with uniform Beta priors. ... t was set for the Fourier approach, so that stealth was achieved 90% of the time those times that contributed to the plot. ... varying prior precision b (inverse of covariance) and weights with bounded norm 10/ b |