InterpretML: A Unified Framework for Machine Learning Interpretability. - Harsha Nori; Samuel Jenkins; Paul Koch; Rich Caruana (CoRR, 2019)

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. - Rich Caruana; Scott M. Lundberg; Marco Túlio Ribeiro; Harsha Nori; Samuel Jenkins (KDD, 2020)

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. - Harmanpreet Kaur; Harsha Nori; Samuel Jenkins; Rich Caruana; Hanna M. Wallach; Jennifer Wortman Vaughan (DaSH@KDD, 2021)

Generating Structured Outputs from Language Models: Benchmark and Studies. - Saibo Geng; Hudson Cooper; Michal Moskal; Samuel Jenkins; Julian Berman; Nathan Ranchin; Robert West 001; Eric Horvitz; Harsha Nori (CoRR, 2025)