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SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions.
Comprehensive OOD Detection Improvements
As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for giving context to predictions.
Dimensionality Reduction
Out of Distribution (OOD) Detection
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.
The Challenge of Differentially Private Screening Rules
Linear $L_1$-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 with TF-IDF or Okapi feature values are a strong and easy baseline.
