Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. - John Sipple (ICML, 2020)

A General-Purpose Method for Applying Explainable AI for Anomaly Detection. - John Sipple; Abdou Youssef (ISMIS, 2022)

A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings. - Judah A. Goldfeder; John A. Sipple (BuildSys, 2023)

Reducing Carbon Emissions at Scale: Interpretable and Efficient to Implement Reinforcement Learning via Policy Extraction. - Judah Goldfeder; John Sipple (BuildSys, 2024)

Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability. - Judah Goldfeder; John Sipple (CoRR, 2024)