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Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.

T. Bosschieter, Z. Xu, H. Lan, B. Lengerich, H. Nori, K. Sitcov, V. Souter, and R. Caruana. CoRR, (2022)

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Hasima Nori

Nori Möding

Nori Görmer

Wafa Nori

Harsha Mahabaleshwar

Other publications of authors with the same name

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data.Z. Chen, S. Tan, H. Nori, K. Inkpen, Y. Lou, and R. Caruana. PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, page 534-551. Springer, (2021)Comparing Population Means Under Local Differential Privacy: With Significance and Power.B. Ding, H. Nori, P. Li, and J. Allen. AAAI, page 26-33. AAAI Press, (2018)Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.H. Nori, Y. Lee, S. Zhang, D. Carignan, R. Edgar, N. Fusi, N. King, J. Larson, Y. Li, W. Liu and 8 other author(s). CoRR, (2023)Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.T. Bosschieter, Z. Xu, H. Lan, B. Lengerich, H. Nori, K. Sitcov, V. Souter, and R. Caruana. CoRR, (2022)Differentially Private Synthetic Data via Foundation Model APIs 2: Text.C. Xie, Z. Lin, A. Backurs, S. Gopi, D. Yu, H. Inan, H. Nori, H. Jiang, H. Zhang, Y. Lee and 2 other author(s). CoRR, (2024)Accuracy, Interpretability, and Differential Privacy via Explainable Boosting.H. Nori, R. Caruana, Z. Bu, J. Shen, and J. Kulkarni. ICML, volume 139 of Proceedings of Machine Learning Research, page 8227-8237. PMLR, (2021)Primo: Practical Learning-Augmented Systems with Interpretable Models.Q. Hu, H. Nori, P. Sun, Y. Wen, and T. Zhang. USENIX ATC, page 519-538. USENIX Association, (2022)Sparks of Artificial General Intelligence: Early experiments with GPT-4S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. Lee, Y. Li, S. Lundberg and 4 other author(s). PDF, (March 2023)cite arxiv:2303.12712.Differentially Private Synthetic Data via Foundation Model APIs 1: Images.Z. Lin, S. Gopi, J. Kulkarni, H. Nori, and S. Yekhanin. CoRR, (2023)Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values.Z. Wang, A. Kale, H. Nori, P. Stella, M. Nunnally, D. Chau, M. Vorvoreanu, J. Vaughan, and R. Caruana. KDD, page 4132-4142. ACM, (2022)

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Disambiguation of "Nori, Harsha"

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Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.

Please choose a person to relate this publication to

Hasima Nori

Nori Möding

Nori Görmer

Wafa Nori

Harsha Mahabaleshwar

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nori, Harsha"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.

Please choose a person to relate this publication to

Hasima Nori

Nori Möding

Nori Görmer

Wafa Nori

Harsha Mahabaleshwar

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.