Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices. - Peter M. VanNostrand; Ioannis Kyriazis; Michelle Cheng; Tian Guo 001; Robert J. Walls (CoRR, 2019)

ELITE: Robust Deep Anomaly Detection with Meta Gradient. - Huayi Zhang; Lei Cao 004; Peter M. VanNostrand; Samuel Madden 001; Elke A. Rundensteiner (KDD, 2021)

A Demonstration of AutoOD: A Self-tuning Anomaly Detection System. - Dennis M. Hofmann; Peter M. VanNostrand; Huayi Zhang; Yizhou Yan; Lei Cao 004; Samuel Madden 001; Elke A. Rundensteiner (Proc. VLDB Endow., 2022)

FACET: Robust Counterfactual Explanation Analytics. - Peter M. VanNostrand; Huayi Zhang; Dennis M. Hofmann; Elke A. Rundensteiner (Proc. ACM Manag. Data, 2023)

Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explanation Type and Presentation on Lay User Understanding. - Peter M. VanNostrand; Dennis M. Hofmann; Lei Ma; Elke A. Rundensteiner (FAccT, 2024)

Counterfactual Explanation Analytics: Empowering Lay Users to Take Action Against Consequential Automated Decisions. - Peter M. VanNostrand; Dennis M. Hofmann; Lei Ma; Belisha Genin; Randy Huang; Elke A. Rundensteiner (Proc. VLDB Endow., 2024)

Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data. - Lei Ma; Lei Cao 004; Peter M. VanNostrand; Dennis M. Hofmann; Yao Su 003; Elke A. Rundensteiner (Proc. ACM Manag. Data, 2024)

Agree to Disagree: Robust Anomaly Detection with Noisy Labels. - Dennis M. Hofmann; Peter M. VanNostrand; Lei Ma; Huayi Zhang; Joshua DeOliveira; Lei Cao 004; Elke A. Rundensteiner (Proc. ACM Manag. Data, 2025)