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Bridging the Lexical Chasm: Statistical Approaches to Answer-Finding, , , , , and . In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, (2000)On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks., , and . CoRR, (2020)The automatic training of rule bases that use numerical uncertainty representations.. Int. J. Approx. Reason., 2 (3): 330-331 (1988)Benefitting from the Variables that Variable Selection Discards., and . J. Mach. Learn. Res., (2003)Obtaining Calibrated Probabilities from Boosting, and . CoRR, (2012)Data Science with LLMs and Interpretable Models., , , and . CoRR, (2024)Introduction to the Special Issue ACM SIGKDD 2012., , , and . ACM Trans. Knowl. Discov. Data, 7 (3): 9:1-9:2 (2013)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)Active Learning with Model Selection., , and . AAAI, page 1673-1679. AAAI Press, (2014)Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping., , and . NIPS, page 402-408. MIT Press, (2000)