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Found 7 papers, 1 papers with code
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
In this paper, we approach the problem of uncertainty quantification in deep learning through a predictive framework, which captures uncertainty in model parameters by specifying our assumptions about the predictive distribution of unseen future data.
SMGRL: Scalable Multi-resolution Graph Representation Learning
The resulting multi-resolution embeddings can be aggregated to yield high-quality node embeddings that capture both long- and short-range dependencies.
Federating Recommendations Using Differentially Private Prototypes
However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn such a model without accessing the sensitive data, and without inadvertently leaking private information?
Directing Generative Networks with Weighted Maximum Mean Discrepancy
In this paper, we construct an estimator for the MMD between P and Q when we only have access to P via some biased sample selection mechanism, and suggest methods for estimating this sample selection mechanism when it is not already known.
Slice sampling normalized kernel-weighted completely random measure mixture models
A number of dependent nonparametric processes have been proposed to model non-stationary data with unknown latent dimensionality.
A survey of non-exchangeable priors for Bayesian nonparametric models
Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space.
