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SEALion: a Framework for Neural Network Inference on Encrypted Data
We present SEALion: an extensible framework for privacy-preserving machi...
04/29/2019 ∙ by Tim van Elsloo, et al. ∙
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Three Tools for Practical Differential Privacy
Differentially private learning on real-world data poses challenges for ...
12/07/2018 ∙ by Koen Lennart van der Veen, et al. ∙
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Sinkhorn AutoEncoders
Optimal Transport offers an alternative to maximum likelihood for learni...
10/02/2018 ∙ by Giorgio Patrini, et al. ∙
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Entity Resolution and Federated Learning get a Federated Resolution
Consider two data providers, each maintaining records of different featu...
03/11/2018 ∙ by Richard Nock, et al. ∙
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Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networ...
09/13/2016 ∙ by Giorgio Patrini, et al. ∙
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The Crossover Process: Learnability and Data Protection from Inference Attacks
It is usual to consider data protection and learnability as conflicting ...
06/13/2016 ∙ by Richard Nock, et al. ∙
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Loss factorization, weakly supervised learning and label noise robustness
We prove that the empirical risk of most well-known loss functions facto...
02/08/2016 ∙ by Giorgio Patrini, et al. ∙
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Giorgio Patrini
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University of Amsterdam
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Australian National University
