1 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Edward Raff, Amol Khanna, Fred Lu
To the best of our knowledge, there are no methods today for training differentially private regression models on sparse input data. To remedy this, we adapt the Frank-Wolfe algori…
Sparse Private LASSO Logistic Regression
Amol Khanna, Fred Lu, Edward Raff +1
LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions.…
The Challenge of Differentially Private Screening Rules
Amol Khanna, Fred Lu, Edward Raff
Linear -regularized models have remained one of the simplest and most effective tools in data analysis, especially in information retrieval problems where n-grams over text wi…