Harsha Nori

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

Publication Project

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

Publication Publication Github Project

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

Publication

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

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

AIES | August 2023

Publication

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

Publication Video Project

Capabilities of GPT-4 on Medical Challenge Problems

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

March 2023

Publication PDF DOI

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

Publication Project

Differentially Private Estimation of Heterogeneous Causal Effects

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

CLeaR 2022 | April 2022

PDF

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

Publication Github

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

Publication Project

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

Publication Github Project

InterpretML: A Unified Framework for Machine Learning Interpretability

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

September 2019

Publication Github

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

Publication Publication Project Project

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

Publication Project

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

Publication Publication Github Project

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

Publication Project

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

Publication Publication Github Project

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

Publication

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

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

AIES | August 2023

Publication

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

Publication Video Project

Capabilities of GPT-4 on Medical Challenge Problems

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

March 2023

Publication PDF DOI

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

Publication Project

Differentially Private Estimation of Heterogeneous Causal Effects

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

CLeaR 2022 | April 2022

PDF

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

Publication Github

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

Publication Project

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

Publication Github Project

InterpretML: A Unified Framework for Machine Learning Interpretability

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

September 2019

Publication Github

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

Publication Project

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

Publication Publication Github Project

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

Publication Project

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

Publication Publication Github Project

Differentially Private Estimation of Heterogeneous Causal Effects

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

CLeaR 2022 | April 2022

PDF

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

Publication Publication Project Project

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

Publication

Capabilities of GPT-4 on Medical Challenge Problems

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

March 2023

Publication PDF DOI

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

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

AIES | August 2023

Publication

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

Publication Github

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

Publication Github Project

Differentially Private Estimation of Heterogeneous Causal Effects

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

CLeaR 2022 | April 2022

PDF

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

Publication Project

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

Publication Publication Github Project

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

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

AIES | August 2023

Publication

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

Publication Project

Differentially Private Estimation of Heterogeneous Causal Effects

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

CLeaR 2022 | April 2022

PDF

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

Publication Github

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

Publication Project

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

Publication Github Project

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

Publication Publication Project Project

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

Publication

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

Publication Video Project

Capabilities of GPT-4 on Medical Challenge Problems

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

March 2023

Publication PDF DOI

InterpretML: A Unified Framework for Machine Learning Interpretability

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

September 2019

Publication Github