632 citations · 665 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Combining Public and Private Data
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
Differential privacy is widely adopted to provide provable privacy guarantees in data analysis. We consider the problem of combining public and private data (and, more generally, d…
Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, provid…
Markov Determinantal Point Processes
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox
A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contai…