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Author search results
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- Rich Caruana
aka: Richard Caruana, Richard A. Caruana
Microsoft Research, Redmond, WA, USA
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Publication search results
found 150 matches
- 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): 65-87 (2024) - Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao:
Rethinking Interpretability in the Era of Large Language Models. CoRR abs/2402.01761 (2024) - Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana:
Data Science with LLMs and Interpretable Models. CoRR abs/2402.14474 (2024) - Sebastian Bordt, Harsha Nori, Rich Caruana:
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. CoRR abs/2403.06644 (2024) - 2023
- Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana:
Considerations when learning additive explanations for black-box models. Mach. Learn. 112(9): 3333-3359 (2023) - Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau:
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CHI 2023: 835:1-835:20 - Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau:
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CoRR abs/2302.14165 (2023) - Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - Daniel Schug, Sai S. Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak:
Extending Explainable Boosting Machines to Scientific Image Data. CoRR abs/2305.16526 (2023) - Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs:
Diagnosis Uncertain Models For Medical Risk Prediction. CoRR abs/2306.17337 (2023) - 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 abs/2308.01157 (2023) - 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. CoRR abs/2310.10203 (2023) - Odelia Melamed, Rich Caruana:
Explaining high-dimensional text classifiers. CoRR abs/2311.13454 (2023) - 2022
- Du Wang, Sang Ho Lee, Huaizhi Geng, Haoyu Zhong, John Plastaras, Andrzej Wojcieszynski, Richard Caruana, Ying Xiao:
Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. Frontiers Artif. Intell. 5 (2022) - Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana:
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study. J. Biomed. Informatics 130: 104086 (2022) - Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
Dropout as a Regularizer of Interaction Effects. AISTATS 2022: 7550-7564 - Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan:
Differentially Private Estimation of Heterogeneous Causal Effects. CLeaR 2022: 618-633 - Chun-Hao Chang, Rich Caruana, Anna Goldenberg:
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. ICLR 2022 - Rich Caruana, Harsha Nori:
Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. KDD 2022: 4776-4777 - 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: 4132-4142 - Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan:
Differentially Private Estimation of Heterogeneous Causal Effects. CoRR abs/2202.11043 (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. CoRR abs/2206.15465 (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 abs/2207.05322 (2022) - Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana:
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML. CoRR abs/2211.08991 (2022) - 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): 14-27 (2021) - Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs:
Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. AMIA 2021 - Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML 2021: 8227-8237 - Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? KDD 2021: 95-105 - 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
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