Multi-task classification with sequential instances and tasks

In this paper, we propose a novel multi-task classification framework, called Multi-Task classification with Sequential Instances and Tasks (MTSIT). Different from previous works, which treat all tasks and instances equally, MTSIT is inspired by the cognitive process of human brain that often learns from easier tasks to harder tasks. Specifically, the method attempts to jointly learn the task curriculum (learning order of tasks) and the instance curriculum (learning order of instances) by introducing a self-paced item for the instances of each task in the existing multi-task learning framework Sequential Multi-Task learning (SeqMT), which transfers information from the previously learned tasks to the next ones through shared task parameters. To effectively solve MTSIT, we also propose an optimization algorithm in which the instance curriculum and the task curriculum alternate between two paradigms, Tasks-to-Instances and Instances-to-Tasks (TIIT). In the tasks-to-instances step, the learner conducts the instance curriculum when the task curriculum has been fixed, while in the instances-to-tasks step, the task curriculum is learned when the instance curriculum in each task has been settled down. Our TIIT method is based on an error bound of the proposed MTSIT. Experimental results on three real world datasets demonstrate the effectiveness of our method.