Comparing Population Means Under Local Differential Privacy: With Significance and Power. - Bolin Ding; Harsha Nori; Paul Li; Joshua Allen (AAAI, 2018)

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. - Joshua Allen; Bolin Ding; Janardhan Kulkarni; Harsha Nori; Olga Ohrimenko; Sergey Yekhanin (NeurIPS, 2019)

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)

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. - Harsha Nori; Rich Caruana; Zhiqi Bu; Judy Hanwen Shen; Janardhan Kulkarni (ICML, 2021)

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)

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. - Zhi Chen 009; Sarah Tan; Harsha Nori; Kori Inkpen; Yin Lou; Rich Caruana (PKDD/ECML Workshops (1), 2021)

GAM Changer: Editing Generalized Additive Models with Interactive Visualization. - Zijie J. Wang; Alex Kale; Harsha Nori; Peter Stella; Mark E. Nunnally; Duen Horng Chau; Mihaela Vorvoreanu; Jennifer Wortman Vaughan; Rich Caruana (CoRR, 2021)

Summarize with Caution: Comparing Global Feature Attributions. - Alex Okeson; Rich Caruana; Nick Craswell; Kori Inkpen; Scott M. Lundberg; Harsha Nori; Hanna M. Wallach; Jennifer Wortman Vaughan (IEEE Data Eng. Bull., 2021)

Differentially Private Estimation of Heterogeneous Causal Effects. - Fengshi Niu; Harsha Nori; Brian Quistorff; Rich Caruana; Donald Ngwe; Aadharsh Kannan (CLeaR, 2022)

Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. - Rich Caruana; Harsha Nori (KDD, 2022)

Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. - Zijie J. Wang; Alex Kale; Harsha Nori; Peter Stella; Mark E. Nunnally; Duen Horng Chau; Mihaela Vorvoreanu; Jennifer Wortman Vaughan; Rich Caruana (KDD, 2022)

Primo: Practical Learning-Augmented Systems with Interpretable Models. - Qinghao Hu 004; Harsha Nori; Peng Sun 006; Yonggang Wen 001; Tianwei Zhang 004 (USENIX ATC, 2022)

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. - Tomas M. Bosschieter; Zifei Xu; Hui Lan; Benjamin J. Lengerich; Harsha Nori; Kristin Sitcov; Vivienne Souter; Rich Caruana (CoRR, 2022)

Supporting Human-AI Collaboration in Auditing LLMs with LLMs. - Charvi Rastogi; Marco Túlio Ribeiro; Nicholas King; Harsha Nori; Saleema Amershi (AIES, 2023)

Sparks of Artificial General Intelligence: Early experiments with GPT-4. - Sébastien Bubeck; Varun Chandrasekaran; Ronen Eldan; Johannes Gehrke; Eric Horvitz; Ece Kamar; Peter Lee; Yin Tat Lee; Yuanzhi Li; Scott M. Lundberg; Harsha Nori; Hamid Palangi; et al. (CoRR, 2023)

Capabilities of GPT-4 on Medical Challenge Problems. - Harsha Nori; Nicholas King; Scott Mayer McKinney; Dean Carignan; Eric Horvitz (CoRR, 2023)

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. - Benjamin J. Lengerich; Sebastian Bordt; Harsha Nori; Mark E. Nunnally; Yin Aphinyanaphongs; Manolis Kellis; Rich Caruana (CoRR, 2023)

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. - Harsha Nori; Yin Tat Lee; Sheng Zhang 012; Dean Carignan; Richard Edgar; Nicolò Fusi; Nicholas King; Jonathan Larson; Yuanzhi Li; Weishung Liu; Renqian Luo; Scott Mayer McKinney; et al. (CoRR, 2023)

Differentially Private Synthetic Data via Foundation Model APIs 1: Images. - Zinan Lin 001; Sivakanth Gopi; Janardhan Kulkarni; Harsha Nori; Sergey Yekhanin (ICLR, 2024)

Differentially Private Synthetic Data via Foundation Model APIs 2: Text. - Chulin Xie; Zinan Lin 001; Arturs Backurs; Sivakanth Gopi; Da Yu; Huseyin A. Inan; Harsha Nori; Haotian Jiang; Huishuai Zhang; Yin Tat Lee; Bo Li 026; Sergey Yekhanin (ICML, 2024)

Data Science with LLMs and Interpretable Models. - Sebastian Bordt; Benjamin J. Lengerich; Harsha Nori; Rich Caruana (CoRR, 2024)

Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. - Sebastian Bordt; Harsha Nori; Rich Caruana (CoRR, 2024)

Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. - Sebastian Bordt; Harsha Nori; Vanessa Rodrigues; Besmira Nushi; Rich Caruana (CoRR, 2024)

GAMformer: In-Context Learning for Generalized Additive Models. - Andreas Mueller; Julien Siems; Harsha Nori; David Salinas; Arber Zela; Rich Caruana; Frank Hutter (CoRR, 2024)

From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond. - Harsha Nori; Naoto Usuyama; Nicholas King; Scott Mayer McKinney; Xavier Fernandes; Sheng Zhang 012; Eric Horvitz (CoRR, 2024)

Steering Language Model Refusal with Sparse Autoencoders. - Kyle O'Brien; David Majercak; Xavier Fernandes; Richard Edgar; Jingya Chen; Harsha Nori; Dean Carignan; Eric Horvitz; Forough Poursabzi-Sangdeh (CoRR, 2024)

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. - Tomas M. Bosschieter; Zifei Xu; Hui Lan; Benjamin J. Lengerich; Harsha Nori; Ian S. Painter; Vivienne Souter; Rich Caruana (J. Heal. Informatics Res., 2024)

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)

Sequential Diagnosis with Language Models. - Harsha Nori; Mayank Daswani; Christopher Kelly; Scott M. Lundberg; Marco Túlio Ribeiro; Marc Wilson; Xiaoxuan Liu; Viknesh Sounderajah; Jonathan Carlson; Matthew P. Lungren; Bay Gross; Peter Hames; et al. (CoRR, 2025)