most citedAccurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CR2024

Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference

Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju

Predictive machine learning models are becoming increasingly deployed in high-stakes contexts involving sensitive personal data; in these contexts, there is a trade-off between mod…

cs.LG2023★ 1 cited

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

Catherine Huang, Chelse Swoopes, Christina Xiao +2

Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who rece…