381 citations · 2.3k across the 89 of their papers we have counts for
8 papers · 2 filters
A New Analysis of Differential Privacy's Generalization Guarantees
Christopher Jung, Katrina Ligett, Seth Neel +3
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially priva…
Oracle Efficient Private Non-Convex Optimization
Seth Neel, Aaron Roth, Giuseppe Vietri +1
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deri…
Exponential Separations in Local Differential Privacy
Matthew Joseph, Jieming Mao, Aaron Roth
We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connec…
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
Ryan Rogers, Aaron Roth, Adam Smith +3
We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates. Prior work in this area…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more com…
Average Individual Fairness: Algorithms, Generalization and Experiments
Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
We propose a new family of fairness definitions for classification problems that combine some of the best properties of both statistical and individual notions of fairness. We posi…