To be Robust or to be Fair: Towards Fairness in Adversarial Training
Authors: Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang | Published: 2020-10-13 | Updated: 2021-05-18
TextHide: Tackling Data Privacy in Language Understanding Tasks
Authors: Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora | Published: 2020-10-12
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
Authors: David Byrd, Antigoni Polychroniadou | Published: 2020-10-12
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
Authors: Nader Sehatbakhsh, Ellie Daw, Onur Savas, Amin Hassanzadeh, Ian McCulloh | Published: 2020-10-12
ByzShield: An Efficient and Robust System for Distributed Training
Authors: Konstantinos Konstantinidis, Aditya Ramamoorthy | Published: 2020-10-10 | Updated: 2021-03-04
How Does Mixup Help With Robustness and Generalization?
Authors: Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou | Published: 2020-10-09 | Updated: 2021-03-17
Anomaly detection with superexperts under delayed feedback
Authors: Raisa Dzhamtyrova, Carsten Maple | Published: 2020-10-08 | Updated: 2021-04-22
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
Authors: Federico Barbero, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro | Published: 2020-10-08 | Updated: 2024-01-08
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems
Authors: AKM Iqtidar Newaz, Nur Imtiazul Haque, Amit Kumar Sikder, Mohammad Ashiqur Rahman, A. Selcuk Uluagac | Published: 2020-10-07
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Authors: Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, Pushmeet Kohli | Published: 2020-10-07 | Updated: 2021-03-30