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Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data., , , , , и . PKDD/ECML Workshops (1), том 1524 из Communications in Computer and Information Science, стр. 534-551. Springer, (2021)Comparing Population Means Under Local Differential Privacy: With Significance and Power., , , и . AAAI, стр. 26-33. AAAI Press, (2018)Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine., , , , , , , , , и 8 other автор(ы). CoRR, (2023)Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes., , , , , , , и . CoRR, (2022)Accuracy, Interpretability, and Differential Privacy via Explainable Boosting., , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 8227-8237. PMLR, (2021)Primo: Practical Learning-Augmented Systems with Interpretable Models., , , , и . USENIX ATC, стр. 519-538. USENIX Association, (2022)Differentially Private Synthetic Data via Foundation Model APIs 2: Text., , , , , , , , , и 2 other автор(ы). CoRR, (2024)Sparks of Artificial General Intelligence: Early experiments with GPT-4, , , , , , , , , и 4 other автор(ы). PDF, (марта 2023)cite arxiv:2303.12712.Differentially Private Synthetic Data via Foundation Model APIs 1: Images., , , , и . CoRR, (2023)Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning., , , , , и . DaSH@KDD, (2021)