73 citations · 585 across the 58 of their papers we have counts for
7 papers · 1 filter
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao +4
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attri…
The Structure of Optimal Private Tests for Simple Hypotheses
Clément L. Canonne, Gautam Kamath, Audra McMillan +2
Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privat…
Observation of Multiphoton Frequency Conversion in Superconducting Circuits
H. Z. Jooya, G. Sun, J. Pan +3
Multiphoton up/down conversion in a transmon circuit, driven by a pair of microwaves tuned near and far off the qubit resonance, has been observed. The experimental realization of…
The Limits of Post-Selection Generalization
Kobbi Nissim, Adam Smith, Thomas Steinke +2
While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the…
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal +1
We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and lear…
Local Differential Privacy for Evolving Data
Matthew Joseph, Aaron Roth, Jonathan Ullman +1
There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the dat…