17 citations · 49 across the 16 of their papers we have counts for
3 papers · 1 filter
Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6
Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…
Algorithms that Approximate Data Removal: New Results and Limitations
Vinith M. Suriyakumar, Ashia C. Wilson
We study the problem of deleting user data from machine learning models trained using empirical risk minimization. Our focus is on learning algorithms which return the empirical ri…
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun
Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are…