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An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.

, , , , and . KDD, page 4089-4099. ACM, (2023)

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Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts., , and . KDD, page 1695-1704. ACM, (2016)Cryo-ZSSR: multiple-image super-resolution based on deep internal learning., , , , , , and . CoRR, (2020)Learning Certifiably Optimal Rule Lists for Categorical Data., , , , and . J. Mach. Learn. Res., (2017)Regulating Greed Over Time in Multi-Armed Bandits., , and . J. Mach. Learn. Res., (2021)The age of secrecy and unfairness in recidivism prediction., , and . CoRR, (2018)Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods., , , and . CICLing, volume 5449 of Lecture Notes in Computer Science, page 86-97. Springer, (2009)Machine Learning for theNew York City Power Grid., , , , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 34 (2): 328-345 (2012)Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model, , , and . (2015)cite arxiv:1511.01644Comment: Published at http://dx.doi.org/10.1214/15-AOAS848 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org).Multitask Learning for Citation Purpose Classification., , , , , , and . CoRR, (2021)Interpretable Almost Matching Exactly With Instrumental Variables., , , , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 1116-1126. AUAI Press, (2019)