Censored and Fair Universal Representations using Generative Adversarial Models

By Peter Kairouz, Jiachun Liao, Chong Huang, Lalitha Sankar. 2019

18 citations recorded by the source corpus.

Subfields: Generative Models, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

1. Christian Haas
2. Hadi Esmaeilzadeh
3. Abhishek Singh
4. Simon Caton
5. Mohammadkazem Taram

In AI Ethics, Fairness & Society

1. Christian Haas
2. Simon Caton

Citations in the corpus

• Privacy in Deep Learning: A Survey (2020, background): “ds for both privacy and fairness, where privacy and fairness are provided through information degradation, through obfuscation or adversarial learning and demonstrated by mutual information reduction [82, 83, 59, 84, 85, 86, 87, 88, 89, 90, 91]. Huang et al. introduce a context-aware privacy framewo”
• Fairness in Machine Learning: A Survey (2020, methodology): “[277, 194, 97, 278, 3, 152]) with similar objectives to transformation as discussed in subsection 4.”