1 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Hidden Technical Debts for Fair Machine Learning in Financial Services
Chong Huang, Arash Nourian, Kevin Griest
The recent advancements in machine learning (ML) have demonstrated the potential for providing a powerful solution to build complex prediction systems in a short time. However, in…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…
Generative Adversarial Privacy
Chong Huang, Peter Kairouz, Xiao Chen +2
We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holde…
Context-Aware Generative Adversarial Privacy
Chong Huang, Peter Kairouz, Xiao Chen +2
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions,…