Harsha Nori

According to our database, Harsha Nori authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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., March, 2024

Elephants Never Forget: Testing Language Models for Memorization of Tabular Data.

Sebastian Bordt, Harsha Nori, Rich Caruana

CoRR, 2024

Differentially Private Synthetic Data via Foundation Model APIs 2: Text.

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

CoRR, 2024

Data Science with LLMs and Interpretable Models.

Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana

CoRR, 2024

2023

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

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

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

Differentially Private Synthetic Data via Foundation Model APIs 1: Images.

Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin

CoRR, 2023

Capabilities of GPT-4 on Medical Challenge Problems.

Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz

CoRR, 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, Marco Túlio Ribeiro, Yi Zhang

CoRR, 2023

Supporting Human-AI Collaboration in Auditing LLMs with LLMs.

Charvi Rastogi, Marco Túlio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

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

Primo: Practical Learning-Augmented Systems with Interpretable Models.

Qinghao Hu, Harsha Nori, Peng Sun, Yonggang Wen, Tianwei Zhang

Proceedings of the 2022 USENIX Annual Technical Conference, 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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation.

Rich Caruana, Harsha Nori

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Differentially Private Estimation of Heterogeneous Causal Effects.

Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

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

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

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data.

Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 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

Proceedings of the 3rd Workshop on Data Science with Human in the Loop, 2021

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting.

Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges.

Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

InterpretML: A Unified Framework for Machine Learning Interpretability.

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

CoRR, 2019

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors.

Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Comparing Population Means Under Local Differential Privacy: With Significance and Power.

Bolin Ding, Harsha Nori, Paul Li, Joshua Allen

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018