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Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes.

, , , , , , , and . J. Heal. Informatics Res., 8 (1): 65-87 (March 2024)

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Data Science with LLMs and Interpretable Models., , , and . CoRR, (2024)Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data., , , , , and . 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., , , and . AAAI, page 26-33. AAAI Press, (2018)Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine., , , , , , , , , and 8 other author(s). CoRR, (2023)LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs., , , , , , and . CoRR, (2023)Elephants Never Forget: Testing Language Models for Memorization of Tabular Data., , and . CoRR, (2024)Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes., , , , , , , and . CoRR, (2023)Differentially Private Estimation of Heterogeneous Causal Effects., , , , , and . CLeaR, volume 177 of Proceedings of Machine Learning Research, page 618-633. PMLR, (2022)Supporting Human-AI Collaboration in Auditing LLMs with LLMs., , , , and . AIES, page 913-926. ACM, (2023)Differentially Private Synthetic Data via Foundation Model APIs 2: Text., , , , , , , , , and 2 other author(s). CoRR, (2024)