Search Results for author: Wilko Henecka

Found 3 papers, 0 papers with code

Boosted and Differentially Private Ensembles of Decision Trees

no code implementations26 Jan 2020 Richard Nock, Wilko Henecka

To address this, we craft a new parametererized proper loss, called the M$\alpha$-loss, which, as we show, allows to finely tune the tradeoff in the complete spectrum of sensitivity vs boosting guarantees.

Entity Resolution and Federated Learning get a Federated Resolution

no code implementations11 Mar 2018 Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne

In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains.

Entity Resolution Federated Learning +1

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

no code implementations29 Nov 2017 Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne

Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.

Entity Resolution Federated Learning +1

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