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Variational Importance Sampling: Initial Findings., , and . CCIA, volume 319 of Frontiers in Artificial Intelligence and Applications, page 95-104. IOS Press, (2019)Crowd Learning with Candidate Labeling: An EM-Based Solution., , and . CAEPIA, volume 11160 of Lecture Notes in Computer Science, page 13-23. Springer, (2018)A framework for evaluation in learning from label proportions.. Progress in AI, 8 (3): 359-373 (2019)Learning from Proportions of Positive and Unlabeled Examples., , and . Int. J. Intell. Syst., 32 (2): 109-133 (2017)Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions., and . CAEPIA, volume 7023 of Lecture Notes in Computer Science, page 134-144. Springer, (2011)Learning from Crowds in Multi-dimensional Classification Domains., , and . CAEPIA, volume 8109 of Lecture Notes in Computer Science, page 352-362. Springer, (2013)Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve.. CAEPIA, volume 11160 of Lecture Notes in Computer Science, page 76-86. Springer, (2018)Aggregated outputs by linear models: An application on marine litter beaching prediction., , , , , and . Inf. Sci., (2019)Learning Bayesian network classifiers from label proportions., , and . Pattern Recognition, 46 (12): 3425-3440 (2013)Weak supervision and other non-standard classification problems: A taxonomy., , and . Pattern Recognition Letters, (2016)