{"id":{"repo_id":"uts","oai_identifier":"oai:opus.lib.uts.edu.au:10453/154754"},"canonical_url":"https://search.dev.ndltd.org/etd/uts/oai:opus.lib.uts.edu.au:10453/154754","repository":{"repo_id":"uts","name":"University of Technology Sydney","base_url":"https://opus.lib.uts.edu.au/oai/request"},"display":{"title":"Privileged Machine Learning for Prediction","abstract":"Machine learning for prediction suffers from asymmetric distribution, such as posterior information, future information and hidden information. With some additional information only available in training, how to learn a machine learning model with them remains a key challenge. Despite recent advances in important domains such as vision and medicine, the standard learning under privileged information paradigm does not offer a satisfactory solution for learning variational privileged information. 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