Machine Learning

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The widespread emergence of digital data has created new opportunities both to break the central bottleneck of machine learning for prediction systems caused by the reliance on human-annotated data and to address ethical and security concerns associated with information sharing. This research seeks to develop new information transfer methods that facilitate effective information sharing across diverse and changing real-world scenarios, aiming to reduce dependence on human annotation for supervised learning, while addressing privacy and fairness considerations. The key outcome will be a new generation of information transfer technologies that allow machine learning to be applied in diverse, socially beneficial and environmentally adaptive services.