On Regularizing Rademacher Observation Losses

By Richard Nock. 2016

3 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Stephen Hardy
2. Wilko Henecka
3. Hamish Ivey-Law
4. Richard Nock
5. Guillaume Smith
6. Brian Thorne
7. Yang Liu
8. Qiang Yang

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 t”