We propose PASTE, the first differentially private aggregation algorithms for distributed time-series data that offer good practical utility without any trusted server. PASTE add...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological ...
This paper describes how high level biological knowledge obtained from ontologies such as the Gene Ontology (GO) can be integrated with low level information extracted from a Baye...
This paper describes how high level biological knowledge obtained from ontologies such as the Gene Ontology (GO) can be integrated with low level information extracted from a Baye...
Kenneth McGarry, Sheila Garfield, Nick Morris, Ste...