735 citations · 871 across the 27 of their papers we have counts for
4 papers · 1 filter
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez, Martin Bertran, Guillermo Sapiro
In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a…
Learning to Collaborate for User-Controlled Privacy
Martin Bertran, Natalia Martinez, Afroditi Papadaki +3
It is becoming increasingly clear that users should own and control their data. Utility providers are also becoming more interested in guaranteeing data privacy. As such, users and…
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Qiang Qiu, Xiuyuan Cheng, Robert Calderbank +1
Filters in a Convolutional Neural Network (CNN) contain model parameters learned from enormous amounts of data. In this paper, we suggest to decompose convolutional filters in CNN…
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems
Jure Sokolic, Qiang Qiu, Miguel R. D. Rodrigues +1
Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we see that achieving universally secure, private, and fair syst…