Machine Learning - Special issue on inductive transfer
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Robust multi-task learning with t-processes
Proceedings of the 24th international conference on Machine learning
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A model of inductive bias learning
Journal of Artificial Intelligence Research
Efficient Bayesian task-level transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Multi-task learning utilizes labeled data from other "similar" tasks and can achieve efficient knowledge-sharing between tasks. In this paper, a novel information-theoretic multi-task learning model, i.e. IBMTL, is proposed. The key idea of IBMTL is to minimize the loss mutual information during the classification, while constrain the Kullback Leibler divergence between multiple tasks to some maximal level. The basic trade-off is between maximize the relevant information while minimize the "dissimilarity" between multiple tasks. The IBMTL algorithm is compared with TrAdaBoost which extends AdaBoost for transfer learning. The experiments were conducted on two data sets for transfer learning, Email spam-filtering data set and sentiment classification data set. The experimental results demonstrate that IBMTL outperforms TrAdaBoost.