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2020 – today
- 2021
[c11]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: 8216-8226
2010 – 2019
- 2019
- [c10]Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav
, Max Welling, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. UAI 2019: 733-743 - [i12]Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law:
SEALion: a Framework for Neural Network Inference on Encrypted Data. CoRR abs/1904.12840 (2019) - 2018
- [i11]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 abs/1803.04035 (2018) - [i10]Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. CoRR abs/1810.01118 (2018) - [i9]Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini:
Three Tools for Practical Differential Privacy. CoRR abs/1812.02890 (2018) - 2017
- [c9]Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen:
Tsallis Regularized Optimal Transport and Ecological Inference. AAAI 2017: 2387-2393 - [c8]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: 2233-2241 - [i8]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 abs/1711.10677 (2017) - 2016
- [c7]Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni:
Loss factorization, weakly supervised learning and label noise robustness. ICML 2016: 708-717 - [c6]Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano:
Fast Learning from Distributed Datasets without Entity Matching. IJCAI 2016: 1909-1917 - [i7]Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni:
Loss factorization, weakly supervised learning and label noise robustness. CoRR abs/1602.02450 (2016) - [i6]Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano:
Fast Learning from Distributed Datasets without Entity Matching. CoRR abs/1603.04002 (2016) - [i5]Richard Nock, Giorgio Patrini, Finnian Lattimore, Tibério S. Caetano:
The Crossover Process: Learnability meets Protection from Inference Attacks. CoRR abs/1606.04160 (2016) - [i4]Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu:
Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR abs/1609.03683 (2016) - [i3]Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen:
Tsallis Regularized Optimal Transport and Ecological Inference. CoRR abs/1609.04495 (2016) - 2015
- [c5]Richard Nock, Giorgio Patrini, Arik Friedman:
Rademacher Observations, Private Data, and Boosting. ICML 2015: 948-956 - [i2]Richard Nock, Giorgio Patrini, Arik Friedman:
Rademacher Observations, Private Data, and Boosting. CoRR abs/1502.02322 (2015) - 2014
- [c4]Giorgio Patrini, Richard Nock, Tibério S. Caetano, Paul Rivera:
(Almost) No Label No Cry. NIPS 2014: 190-198 - 2012
- [c3]Nicola Gatti, Giorgio Patrini, Marco Rocco, Tuomas Sandholm:
Combining local search techniques and path following for bimatrix games. UAI 2012: 286-295 - [i1]Nicola Gatti, Giorgio Patrini, Marco Rocco, Tuomas Sandholm:
Combining local search techniques and path following for bimatrix games. CoRR abs/1210.4858 (2012) - 2010
- [c2]Sofia Ceppi, Nicola Gatti, Giorgio Patrini, Marco Rocco:
Local search techniques for computing equilibria in two-player general-sum strategic-form games. AAMAS 2010: 1469-1470 - [c1]Sofia Ceppi, Nicola Gatti, Giorgio Patrini, Marco Rocco:
Local Search Methods for Finding a Nash Equilibrium in Two-Player Games. IAT 2010: 335-342
Coauthor Index
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