381 citations · 733 across the 13 of their papers we have counts for
4 papers · 1 filter
How to Use Heuristics for Differential Privacy
Seth Neel, Aaron Roth, Zhiwei Steven Wu
We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for…
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Michael Kearns, Seth Neel, Aaron Roth +1
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness…
Fair Algorithms for Learning in Allocation Problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung +4
Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objec…
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth Neel, Aaron Roth
Data that is gathered adaptively --- via bandit algorithms, for example --- exhibits bias. This is true both when gathering simple numeric valued data --- the empirical means kept…