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cs.LG2023★ 1 cited
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…
cs.LG2023★ 1 cited
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.…
cs.LG2023★ 1 cited
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…