Sparse Private LASSO Logistic Regression
LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions. Differentially private versions of LASSO logistic regression have been developed, but generally produce dense solutions, reducing the intrinsic utility of the LASSO penalty. In this paper, we present a differentially private method for sparse logistic regression that maintains hard zeros. Our key insight is to first train a non-private LASSO logistic regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection. To demonstrate our method's performance, we run experiments on synthetic and real-world datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionModel SelectionregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
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 algorithm for $L_1$ penalized linear regressio…
regressionA Fast Method for Lasso and Logistic Lasso
We propose a fast method for solving compressed sensing, Lasso regression, and Logistic Lasso regression problems that iteratively runs an appropriate solver using an active set approach. We design a strategy to update t…
compressed sensingregressionQuantum Differentially Private Sparse Regression Learning
The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to…
BIG-bench Machine LearningregressionWeighted Lasso Estimates for Sparse Logistic Regression: Non-asymptotic Properties with Measurement Error
When we are interested in high-dimensional system and focus on classification performance, the $\ell_{1}$-penalized logistic regression is becoming important and popular. However, the Lasso estimates could be problematic…
regressionThe Hessian Screening Rule
Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can be solved. In this paper we present a ne…
regression