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Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine., , , , , , , , , and 8 other author(s). CoRR, (2023)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)Capabilities of GPT-4 on Medical Challenge Problems., , , , and . CoRR, (2023)Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes., , , , , , , and . CoRR, (2022)Differentially Private Synthetic Data via Foundation Model APIs 2: Text., , , , , , , , , and 2 other author(s). CoRR, (2024)InterpretML: A Unified Framework for Machine Learning Interpretability., , , and . CoRR, (2019)Primo: Practical Learning-Augmented Systems with Interpretable Models., , , , and . USENIX Annual Technical Conference, page 519-538. USENIX Association, (2022)Accuracy, Interpretability, and Differential Privacy via Explainable Boosting., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 8227-8237. PMLR, (2021)