A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Robust multi-task learning with t-processes
Proceedings of the 24th international conference on Machine learning
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
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Multi-task learning is a way of bringing inductive transfer studied in human learning to the machine learning community. A central issue in multitask learning is to model the relationships between tasks appropriately and exploit them to aid the simultaneous learning of multiple tasks effectively. While some recent methods model and learn the task relationships from data automatically, only pairwise relationships can be represented by them. In this paper, we propose a new model, called Multi-Task High-Order relationship Learning (MTHOL), which extends in a novel way the use of pairwise task relationships to high-order task relationships. We first propose an alternative formulation of an existing multi-task learning method. Based on the new formulation, we propose a high-order generalization leading to a new prior for the model parameters of different tasks. We then propose a new probabilistic model for multi-task learning and validate it empirically on some benchmark datasets.