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Lifelong Learning with Weighted Majority Votes
Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time.
Multi-task and Lifelong Learning of Kernels
We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier.
Multi-Task Learning with Labeled and Unlabeled Tasks
In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided.
Lifelong Learning with Non-i.i.d. Tasks
In the first case we prove a PAC-Bayesian theorem, which can be seen as a direct generalization of the analogous previous result for the i. i. d.
Curriculum Learning of Multiple Tasks
Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data.
A PAC-Bayesian bound for Lifelong Learning
Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed.
