1 citations · 1 across the 6 of their papers we have counts for
6 papers
Sample Complexity of Peer Prediction
Abdellah Aznag, Robin Bowers, Rachel Cummings +3
Peer prediction seeks to incentivize agents to truthfully report an observed signal by rewarding joint sets of reports without observing a ground truth. Following the generalizatio…
A Complexity Measure for Active Learning in Multi-group Mean Estimation
Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
We study a \emph{max-risk} objective for active learning in a multi-group mean estimation -armed bandits: a learner adaptively allocates a budget of samples across group…
Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
Tianyu Wang, Luhao Zhang, Rachel Cummings
Standard differential privacy imposes uniform privacy constraints across all features, overlooking the inherent distinction between sensitive and insensitive features in practice.…
Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
Rachel Cummings, Alessandro Epasto, Jieming Mao +3
The turnstile continual release model of differential privacy captures scenarios where a privacy-preserving real-time analysis is sought for a dataset evolving through additions an…
Thompson Sampling Itself is Differentially Private
Tingting Ou, Marco Avella Medina, Rachel Cummings
In this work we first show that the classical Thompson sampling algorithm for multi-arm bandits is differentially private as-is, without any modification. We provide per-round priv…
Differentially Private Online Submodular Maximization
Sebastian Perez-Salazar, Rachel Cummings
In this work we consider the problem of online submodular maximization under a cardinality constraint with differential privacy (DP). A stream of submodular functions over a co…