Harsha Nori - RankMe

39 papers A* 9C 1Journal 25Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Generating Structured Outputs from Language Models: Benchmark and Studies. CoRR Saibo Geng, Hudson Cooper, Michal Moskal, Samuel Jenkins, Julian Berman, Nathan Ranchin, Robert West, Eric Horvitz, Harsha Nori
2025	J	`jnl`	Sequential Diagnosis with Language Models. CoRR 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, Mustafa Suleyman, Dominic King, Eric Horvitz
2024	J	`jnl`	Data Science with LLMs and Interpretable Models. CoRR Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana
2024	A*	`conf`	Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
2024	A*	`conf`	Differentially Private Synthetic Data via Foundation Model APIs 2: Text. ICML 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
2024	J	`jnl`	Differentially Private Synthetic Data via Foundation Model APIs 2: Text. CoRR 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
2024	J	`jnl`	Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. CoRR Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana
2024	J	`jnl`	Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. CoRR Sebastian Bordt, Harsha Nori, Rich Caruana
2024	J	`jnl`	From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond. CoRR Harsha Nori, Naoto Usuyama, Nicholas King, Scott Mayer McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz
2024	J	`jnl`	GAMformer: In-Context Learning for Generalized Additive Models. CoRR Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter
2024	J	`jnl`	Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. J. Heal. Informatics Res. Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
2024	J	`jnl`	Steering Language Model Refusal with Sparse Autoencoders. CoRR Kyle O'Brien, David Majercak, Xavier Fernandes, Richard Edgar, Jingya Chen, Harsha Nori, Dean Carignan, Eric Horvitz, Forough Poursabzi-Sangdeh
2023	J	`jnl`	Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. CoRR 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, Christopher M. White, Eric Horvitz
2023	J	`jnl`	Capabilities of GPT-4 on Medical Challenge Problems. CoRR Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
2023	J	`jnl`	Differentially Private Synthetic Data via Foundation Model APIs 1: Images. CoRR Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
2023	J	`jnl`	Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. CoRR Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
2023	J	`jnl`	LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. CoRR Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
2023	J	`jnl`	Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR 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
2023	C	`conf`	Supporting Human-AI Collaboration in Auditing LLMs with LLMs. AIES Charvi Rastogi, Marco Túlio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi
2022	—	`conf`	Differentially Private Estimation of Heterogeneous Causal Effects. CLeaR Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
2022	J	`jnl`	Differentially Private Estimation of Heterogeneous Causal Effects. CoRR Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
2022	A*	`conf`	Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. KDD Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2022	J	`jnl`	Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. CoRR Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2022	—	`conf`	Primo: Practical Learning-Augmented Systems with Interpretable Models. USENIX ATC Qinghao Hu, Harsha Nori, Peng Sun, Yonggang Wen, Tianwei Zhang
2022	J	`jnl`	Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. CoRR Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana
2022	A*	`conf`	Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. KDD Rich Caruana, Harsha Nori
2021	A*	`conf`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	J	`jnl`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	J	`jnl`	GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021	—	`conf`	Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
2021	J	`jnl`	Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
2021	—	`conf`	Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana
2020	A*	`conf`	Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins
2020	A*	`conf`	Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
2019	A*	`conf`	An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. NeurIPS Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
2019	J	`jnl`	InterpretML: A Unified Framework for Machine Learning Interpretability. CoRR Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
2018	J	`jnl`	An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. CoRR Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
2018	A*	`conf`	Comparing Population Means Under Local Differential Privacy: With Significance and Power. AAAI Bolin Ding, Harsha Nori, Paul Li, Joshua Allen
2018	J	`jnl`	Comparing Population Means under Local Differential Privacy: with Significance and Power. CoRR Bolin Ding, Harsha Nori, Paul Li, Joshua Allen