68 citations · 163 across the 18 of their papers we have counts for
19 papers
Better Private Linear Regression Through Better Private Feature Selection
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Existing work on differentially private linear regression typically assumes that end users can precisely set data bounds or algorithmic hyperparameters. End users often struggle to…
Measuring Re-identification Risk
CJ Carey, Travis Dick, Alessandro Epasto +8
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification r…
Subset-Based Instance Optimality in Private Estimation
Travis Dick, Alex Kulesza, Ziteng Sun +1
We propose a new definition of instance optimality for differentially private estimation algorithms. Our definition requires an optimal algorithm to compete, simultaneously for eve…
Easy Learning from Label Proportions
Robert Istvan Busa-Fekete, Heejin Choi, Travis Dick +2
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class…
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…
Learning-Augmented Private Algorithms for Multiple Quantile Release
Mikhail Khodak, Kareem Amin, Travis Dick +1
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors. We pro…