68 citations · 163 across the 18 of their papers we have counts for
3 papers · 2 filters
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…
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…