Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy. - Amol Khanna; Vincent Schaffer; Gamze Gürsoy; Mark Gerstein (EMBC, 2022)

Differentially Private Logistic Regression with Sparse Solutions. - Amol Khanna; Fred Lu; Edward Raff; Brian Testa (AISec@CCS, 2023)

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. - Edward Raff; Amol Khanna; Fred Lu (NeurIPS, 2023)

The Challenge of Differentially Private Screening Rules. - Amol Khanna; Fred Lu; Edward Raff (CoRR, 2023)

Sparse Private LASSO Logistic Regression. - Amol Khanna; Fred Lu; Edward Raff; Brian Testa (CoRR, 2023)

Feature Selection from Differentially Private Correlations. - Ryan Swope; Amol Khanna; Philip Doldo; Saptarshi Roy; Edward Raff (AISec@CCS, 2024)

SoK: A Review of Differentially Private Linear Models For High-Dimensional Data. - Amol Khanna; Edward Raff; Nathan Inkawhich (SaTML, 2024)

Comprehensive OOD Detection Improvements. - Anish Lakkapragada; Amol Khanna; Edward Raff; Nathan Inkawhich (CoRR, 2024)

Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government. - Amol Khanna; Adam McCormick; André T. Nguyen; Chris Aguirre; Edward Raff (CoRR, 2024)

Differentially Private Iterative Screening Rules for Linear Regression. - Amol Khanna; Fred Lu; Edward Raff (CODASPY, 2025)

Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent. - Philip Doldo; Derek Everett; Amol Khanna; André T. Nguyen; Edward Raff (CVPR, 2025)

Multi-layer Radial Basis Function Networks for Out-of-distribution Detection. - Amol Khanna; Chenyi Ling; Derek Everett; Edward Raff; Nathan Inkawhich (CVPR Workshops, 2025)