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Learning Task Relations in Multi-Task Learning

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In this talk, I present three of my works to learn task relations for multi-task learning. The first work is to learn pairwise relations for multiple tasks based on a task covariance. The second work learns a multi-task kNN classifier where the classification of each data point depends on data points from all the tasks. The third work models tasks in a hierarchical structure and aims to learn the hierarchical structure as well as the model parameters in a principled framework.

This talk is part of the Machine Learning @ CUED series.

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