Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints

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

22 citations recorded by the source corpus.

Subfields: Generative Models

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. Enyan Dai
2. Hadi Esmaeilzadeh
3. Abhishek Singh
4. Mohammadkazem Taram

Citations in the corpus

• Privacy in Deep Learning: A Survey (2020, background): “There is a body of work studying information-theoretic based methods 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 [67, 68, 48, 69, 70, 71, 72, 73, 7”
• Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information (2020, background): “Recently, adversarial debiasing has been proven to be effective in alleviating the bias of representations [3, 11, 27, 31].”