70 citations · 101 across the 9 of their papers we have counts for
15 papers
Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6
Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning
Jiaqi Wang, Roei Schuster, Ilia Shumailov +2
When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE,…
Interpretability in Safety-Critical FinancialTrading Systems
Gabriel Deza, Adelin Travers, Colin Rowat +1
Sophisticated machine learning (ML) models to inform trading in the financial sector create problems of interpretability and risk management. Seemingly robust forecasting models ma…
SoK: Machine Learning Governance
Varun Chandrasekaran, Hengrui Jia, Anvith Thudi +3
The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society. In this paper, we develop the concept of ML governance to…
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples
Adelin Travers, Lorna Licollari, Guanghan Wang +4
Machine learning (ML) models are known to be vulnerable to adversarial examples. Applications of ML to voice biometrics authentication are no exception. Yet, the implications of au…
Markpainting: Adversarial Machine Learning meets Inpainting
David Khachaturov, Ilia Shumailov, Yiren Zhao +2
Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture…