Andrew Trask

According to our database, Andrew Trask authored at least 18 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases.

Emma Bluemke, Tantum Collins, Ben Garfinkel, Andrew Trask

CoRR, 2023

UN Handbook on Privacy-Preserving Computation Techniques.

David W. Archer, Borja de Balle Pigem, Dan Bogdanov, Mark Craddock, Adrià Gascón, Ronald Jansen, Matjaz Jug, Kim Laine, Robert McLellan, Olga Ohrimenko, Mariana Raykova, Andrew Trask, Simon Wardley

CoRR, 2023

2021

End-to-end privacy preserving deep learning on multi-institutional medical imaging.

Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren

Nat. Mach. Intell., 2021

Towards General-purpose Infrastructure for Protecting Scientific Data Under Study.

Andrew Trask, Kritika Prakash

CoRR, 2021

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.

Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

CoRR, 2021

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask

CoRR, 2021

Syft 0.5: A Platform for Universally Deployable Structured Transparency.

Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib, Théo Ryffel, Kritika Prakash, Tom Titcombe, Varun Kumar Khare, Maddie Shang, Ionesio Junior, Animesh Gupta, Jason Paumier, Nahua Kang, Vova Manannikov, Andrew Trask

CoRR, 2021

2020

The future of digital health with federated learning.

Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus H. Maier-Hein, Sébastien Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

npj Digit. Medicine, 2020

Beyond Privacy Trade-offs with Structured Transparency.

Andrew Trask, Emma Bluemke, Ben Garfinkel, Claudia Ghezzou Cuervas-Mons, Allan Dafoe

CoRR, 2020

Privacy-preserving medical image analysis.

Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis

CoRR, 2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims.

Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian K. Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung

CoRR, 2020

Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy.

Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, Andrew Trask

Proceedings of the PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020

2019

Sample Efficient Adaptive Text-to-Speech.

Yutian Chen, Yannis M. Assael, Brendan Shillingford, David Budden, Scott E. Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Çaglar Gülçehre, Aäron van den Oord, Oriol Vinyals, Nando de Freitas

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Scaling shared model governance via model splitting.

Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli

CoRR, 2018

A generic framework for privacy preserving deep learning.

Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach

CoRR, 2018

Neural Arithmetic Logic Units.

Andrew Trask, Felix Hill, Scott E. Reed, Jack W. Rae, Chris Dyer, Phil Blunsom

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2015

sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings.

Andrew Trask, Phil Michalak, John Liu

CoRR, 2015

Modeling Order in Neural Word Embeddings at Scale.

Andrew Trask, David Gilmore, Matthew Russell

Proceedings of the 32nd International Conference on Machine Learning, 2015