Publications

Sensity’s reports

• Giorgio Patrini
  The State Of Deepfakes, 2020: Updates on Statistics and Trends
  Mar 2021
  

• Henry Ajder, Giorgio Patrini, Francesco Cavalli
  Automating Image Abuse: Deepfakes Bots on Telegram
  Oct 2020
  

• Henry Ajder, Giorgio Patrini, Francesco Cavalli, Laurence Cullen
  The State Of Deepfakes, 2019: Landscape, Threats and Impact
  Oct 2019
  

• Giorgio Patrini, Francesco Cavalli
  The State of Deepfakes, 2018: Reality Under Attack
  Dec 2018
  

Conferences

• Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith and Brian Thorne
  The impact of record linkage on learning from feature partitioned data
  ICML 2021 (spotlight) [supp]

• Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
  Sinkhorn autoencoders
  UAI 2019 (oral, acceptance rate 7.3%) [arXiv]

• Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock and Lizhen Qu
  Making deep neural networks robust to label noise: a loss correction approach
  CVPR 2017 (oral, acceptance rate 2.7%) [code] [video] [slides]

• Boris Muzellec, Richard Nock, Giorgio Patrini and Frank Nielsen
  Tsallis regularized optimal transport and ecological inference
  AAAI 2017 [arXiv]

• Giorgio Patrini, Frank Nielsen, Richard Nock and Marcello Carioni
  Loss factorization, weakly supervised learning and label noise robustness
  ICML 2016 [supp] [arXiv] [video] [slides]

• Giorgio Patrini, Richard Nock, Stephen Hardy and Tiberio Caetano
  Fast learning from distributed datasets without entity matching
  IJCAI 2016 [arXiv]

• Richard Nock, Giorgio Patrini and Arik Friedman
  Rademacher observations, private data, and boosting
  ICML 2015 [arXiv] [video] [slides]

• Giorgio Patrini, Richard Nock, Paul Rivera and Tiberio Caetano
  (Almost) no label no cry
  NIPS 2014 (spotlight, acceptance rate 3.7%) [supp] [code]

• Nicola Gatti, Giorgio Patrini, Marco Rocco and Tuomas Sandholm
  Combining local search techniques and path following for bimatrix games
  UAI 2012

• Sofia Ceppi, Nicola Gatti, Giorgio Patrini and Marco Rocco
  Local search methods for finding a Nash equilibrium in two-player games
  IAT 2010

• Sofia Ceppi, Nicola Gatti, Giorgio Patrini and Marco Rocco
  Local search techniques for computing equilibria in two-player general-sum strategic-form games
  AAMAS 2010 (extended abstract)

Workshops, tech reports and notes

• Koen van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini
  Three tools for practical differential privacy
  NeurIPS 2018 workshop on Privacy preserving machine learning

• Mentari Djatmiko, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Maximilian Ott, Giorgio Patrini, Guillaume Smith, Brian Thorne and Dongyao Wu
  Privacy-preserving entity resolution and logistic regression on encrypted data
  ICMl 2017 workshop on Private and secure machine learning [slides]

• Giorgio Patrini, Frank Nielsen and Richard Nock
  Bridging weak supervision and privacy aware learning via sufficient statistics
  NIPS 2015 workshop on Learning and privacy with incomplete data and weak supervision

Pre-prints

• Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law
  SEALion: a framework for neural network inference on encrypted data [arXiv]

• 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 [arXiv]

Thesis

• Giorgio Patrini
  Weakly supervised learning via statistical sufficiency
  Australian National University, 2016, PhD thesis

• Giorgio Patrini and Marco Rocco
  Local search techniques for Nash equilibrium computation with bimatrix game
  Politecnico di Milano, 2012, MSc thesis

Patents

• Richard Nock and Giorgio Patrini
  Learning from distributed data, WO 2016/127218 A1

• Richard Nock, Giorgio Patrini and Tiberio Caetano
  Learning with transformed data, WO 2016/061628 A1

Academic service

Reviewer for ICML 18; CVPR 18, 19; NIPS 15, 16, 17; NIPS privacy workshop 19; JMLR 16, 17; TPAMI 17; Pattern Recognition 17; S+SSPR 16; JMLC 16.

Workshop co-organizer:

• Synthetic realities: deep learning for detecting audiovisual fakes
  ICML 2019

• Learning and privacy with incomplete data and weak supervision
  NIPS 2015 (primary organizer)