Giorgio Patrini

Publication Activity (10 Years)

Years Active: 2010-2021
Publications (10 Years): 16

Top Topics

Differential Privacy

Homomorphic Encryption

Top Venues

Publications

Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
The Impact of Record Linkage on Learning from Feature Partitioned Data. ICML (2021)
Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
Sinkhorn AutoEncoders. UAI (2019)
Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law
SEALion: a Framework for Neural Network Inference on Encrypted Data. CoRR (2019)
Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen
Sinkhorn AutoEncoders. CoRR (2018)
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini
Three Tools for Practical Differential Privacy. CoRR (2018)
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne
Entity Resolution and Federated Learning get a Federated Resolution. CoRR (2018)
Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
Tsallis Regularized Optimal Transport and Ecological Inference. AAAI (2017)
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. CVPR (2017)
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR (2017)
Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
Tsallis Regularized Optimal Transport and Ecological Inference. CoRR (2016)
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
Loss factorization, weakly supervised learning and label noise robustness. ICML (2016)
Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
Fast Learning from Distributed Datasets without Entity Matching. IJCAI (2016)
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR (2016)
Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
Fast Learning from Distributed Datasets without Entity Matching. CoRR (2016)
Richard Nock, Giorgio Patrini, Finnian Lattimore, Tibério S. Caetano
The Crossover Process: Learnability meets Protection from Inference Attacks. CoRR (2016)
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
Loss factorization, weakly supervised learning and label noise robustness. CoRR (2016)
Richard Nock, Giorgio Patrini, Arik Friedman
Rademacher Observations, Private Data, and Boosting. ICML (2015)
Richard Nock, Giorgio Patrini, Arik Friedman
Rademacher Observations, Private Data, and Boosting. CoRR (2015)
Giorgio Patrini, Richard Nock, Tibério S. Caetano, Paul Rivera
(Almost) No Label No Cry. NIPS (2014)
Nicola Gatti, Giorgio Patrini, Marco Rocco, Tuomas Sandholm
Combining local search techniques and path following for bimatrix games. UAI (2012)
Nicola Gatti, Giorgio Patrini, Marco Rocco, Tuomas Sandholm
Combining local search techniques and path following for bimatrix games CoRR (2012)
Sofia Ceppi, Nicola Gatti, Giorgio Patrini, Marco Rocco
Local Search Methods for Finding a Nash Equilibrium in Two-Player Games. IAT (2010)
Sofia Ceppi, Nicola Gatti, Giorgio Patrini, Marco Rocco
Local search techniques for computing equilibria in two-player general-sum strategic-form games. AAMAS (2010)