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Found 5 papers, 2 papers with code
Differentially Private Statistical Inference through $β$-Divergence One Posterior Sampling
To ameliorate this, we propose $\beta$D-Bayes, a posterior sampling scheme from a generalised posterior targeting the minimisation of the $\beta$-divergence between the model and the data generating process.
Mitigating Statistical Bias within Differentially Private Synthetic Data
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data.
Foundations of Bayesian Learning from Synthetic Data
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy or availability constraints.
Generalized Variational Inference: Three arguments for deriving new Posteriors
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences
The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity.
