Harsha Nori

发表

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Janardhan Kulkarni, Bolin Ding, Sergey Yekhanin, 2018, NeurIPS.

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Harmanpreet Kaur, Rich Caruana, Hanna M. Wallach, 2020, CHI.

InterpretML: A Unified Framework for Machine Learning Interpretability pdf

Rich Caruana, Harsha Nori, Paul Koch, 2019, ArXiv.

Fit Interpretable Machine Learning Models [R package interpret version 0.1.26]

Rich Caruana, Harsha Nori, Paul Koch, 2020 .

Comparing Population Means under Local Differential Privacy: with Significance and Power pdf

Bolin Ding, Paul Li, Joshua Allen, 2018, AAAI.

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges

Rich Caruana, Harsha Nori, Samuel Jenkins, 2020, KDD.

GAM Changer: Editing Generalized Additive Models with Interactive Visualization pdf

Rich Caruana, Duen Horng Chau, Mihaela Vorvoreanu, 2021, ArXiv.

Accuracy, Interpretability and Differential Privacy via Explainable Boosting pdf

Janardhan Kulkarni, Rich Caruana, Harsha Nori, 2020 .