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Found 5 papers, 1 papers with code
Differentially Private Diffusion Models Generate Useful Synthetic Images
By privately fine-tuning ImageNet pre-trained diffusion models with more than 80M parameters, we obtain SOTA results on CIFAR-10 and Camelyon17 in terms of both FID and the accuracy of downstream classifiers trained on synthetic data.
Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates
In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predictive of the Dirichlet process mixture model.
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.
On Locality of Local Explanation Models
Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis.
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993).
