1 citations · 3 across the 12 of their papers we have counts for
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Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
Amol Khanna, Adam McCormick, Andre Nguyen +2
In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researc…
Feature Selection from Differentially Private Correlations
Ryan Swope, Amol Khanna, Philip Doldo +2
Data scientists often seek to identify the most important features in high-dimensional datasets. This can be done through -regularized regression, but this can become ineffici…
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna, Edward Raff, Nathan Inkawhich
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differe…
Comprehensive OOD Detection Improvements
Anish Lakkapragada, Amol Khanna, Edward Raff +1
As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for givin…