Harsha Nori

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 33

Top Topics

Machine Learning

Generalized Additive Models

Top Venues

J. Heal. Informatics Res.

Publications

Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 2: Text. CoRR (2024)
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR (2024)
Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. CoRR (2024)
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. J. Heal. Informatics Res. 8 (1) (2024)
Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana
Data Science with LLMs and Interpretable Models. CoRR (2024)
Sebastian Bordt, Harsha Nori, Rich Caruana
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. CoRR (2024)
Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. CoRR (2023)
Charvi Rastogi, Marco Túlio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
Supporting Human-AI Collaboration in Auditing LLMs with LLMs. AIES (2023)
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. CoRR (2023)
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, Marco Túlio Ribeiro, Yi Zhang
Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR (2023)
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
Capabilities of GPT-4 on Medical Challenge Problems. CoRR (2023)
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian Painter, Vivienne Souter, Rich Caruana
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. CoRR (2023)
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolò Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. CoRR (2023)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. CoRR (2022)
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
Differentially Private Estimation of Heterogeneous Causal Effects. CoRR (2022)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. KDD (2022)
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
Differentially Private Estimation of Heterogeneous Causal Effects. CLeaR (2022)
Rich Caruana, Harsha Nori
Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. KDD (2022)
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. CoRR (2022)
Qinghao Hu, Harsha Nori, Peng Sun, Yonggang Wen, Tianwei Zhang
Primo: Practical Learning-Augmented Systems with Interpretable Models. USENIX Annual Technical Conference (2022)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML (2021)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR (2021)
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. 44 (4) (2021)
Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) (2021)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD (2021)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR (2021)
Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins
Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD (2020)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI (2020)
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. NeurIPS (2019)
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
InterpretML: A Unified Framework for Machine Learning Interpretability. CoRR (2019)
Bolin Ding, Harsha Nori, Paul Li, Joshua Allen
Comparing Population Means Under Local Differential Privacy: With Significance and Power. AAAI (2018)
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. CoRR (2018)
Bolin Ding, Harsha Nori, Paul Li, Joshua Allen
Comparing Population Means under Local Differential Privacy: with Significance and Power. CoRR (2018)