Machine Learning - Special issue on inductive transfer
An Introduction to Variational Methods for Graphical Models
Machine Learning
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 Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An Information-Theoretic Approach for Multi-task Learning
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Semi-Supervised Multi-Task Regression
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
IEEE Transactions on Signal Processing
Multi-agent learning by distributed feature extraction
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Integrating low-rank and group-sparse structures for robust multi-task learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse Linear Identifiable Multivariate Modeling
The Journal of Machine Learning Research
Learning output kernels for multi-task problems
Neurocomputing
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
Learning high-order task relationships in multi-task learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of "outlier" tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, t-processes (TP), which are a generalization of Gaussian processes (GP) for multi-task learning. TP allows the system to effectively distinguish good tasks from noisy or outlier tasks. Experiments show that TP not only improves overall system performance, but can also serve as an indicator for the "informativeness" of different tasks.