381 citations · 2.3k across the 89 of their papers we have counts for
109 papers
Oracle Efficient Online Multicalibration and Omniprediction
Sumegha Garg, Christopher Jung, Omer Reingold +1
A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous…
Scalable Membership Inference Attacks via Quantile Regression
Martin Bertran, Shuai Tang, Michael Kearns +3
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can…
Balanced Filtering via Disclosure-Controlled Proxies
Siqi Deng, Emily Diana, Michael Kearns +1
We study the problem of collecting a cohort or set that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at deployment time.…
Improved Differentially Private Regression via Gradient Boosting
Shuai Tang, Sergul Aydore, Michael Kearns +5
We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters -…
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation
Georgy Noarov, Aaron Roth
We make a connection between multicalibration and property elicitation and show that (under mild technical conditions) it is possible to produce a multicalibrated predictor for a c…
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
Travis Dick, Cynthia Dwork, Michael Kearns +4
A reconstruction attack on a private dataset takes as input some publicly accessible information about the dataset and produces a list of candidate elements of . We introduc…