219 citations · 257 across the 4 of their papers we have counts for
5 papers
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh +1
Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often cro…
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage, Shahar Avin, Jasmine Wang +56
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…
Scaling shared model governance via model splitting
Miljan Martic, Jan Leike, Andrew Trask +3
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation. Unfortunately, neither of these techniqu…
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valu…
Modeling Order in Neural Word Embeddings at Scale
Andrew Trask, David Gilmore, Matthew Russell
Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships. The resulting word-level dis…