From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King, S. McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz
November 2024
Harsha Nori
Director, Research Engineering
Publications
Publications by Year
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo 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
November 2023
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
AIES | August 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 Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang
March 2023
Capabilities of GPT-4 on Medical Challenge Problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
March 2023
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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 | July 2022
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
CLeaR 2022 | April 2022
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
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InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
September 2019
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
Publications by Research Area
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From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King, S. McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz
November 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo 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
November 2023
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
AIES | August 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 Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang
March 2023
Capabilities of GPT-4 on Medical Challenge Problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
March 2023
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 | July 2022
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
CLeaR 2022 | April 2022
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
September 2019
-
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King, S. McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz
November 2024
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo 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
November 2023
Capabilities of GPT-4 on Medical Challenge Problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
March 2023
-
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
-
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
CLeaR 2022 | April 2022
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
-
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
AIES | August 2023
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
-
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
CLeaR 2022 | April 2022
Publications by Type
-
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King, S. McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz
November 2024
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo 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
November 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 Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang
March 2023
-
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
AIES | August 2023
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 | July 2022
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
CLeaR 2022 | April 2022
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
-
Capabilities of GPT-4 on Medical Challenge Problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
March 2023
InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
September 2019