Weakly supervised learning via statistical sufficiency

By Giorgio Patrini. 2016

4 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Richard Nock
2. Hamish Ivey-Law
3. Stephen Hardy
4. Brian Thorne
5. Guillaume Smith
6. Wilko Henecka
7. Giorgio Patrini

Citations in the corpus

• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, methodology): “Related methods exploit the fact that one can learn from sufficient statistics for the class instead of examples [Nock et al., 2015, Nock, 2016, Patrini, 2016], many of which do not require entity resolution to be computed, but rather a weak form of entity resolution between groups of observations…”