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
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
Primal-dual subgradient methods for convex problems
Mathematical Programming: Series A and B - Series B - Special Issue: Nonsmooth Optimization and Applications
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Efficient Online and Batch Learning Using Forward Backward Splitting
The Journal of Machine Learning Research
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Fast multi-task learning for query spelling correction
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient online learning for multitask feature selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-task learning with one-class SVM
Neurocomputing
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Multi-task feature selection (MTFS) is an important tool to learn the explanatory features across multiple related tasks. Previous MTFS methods fulfill this task in batch-mode training. This makes them inefficient when data come in sequence or when the number of training data is so large that they cannot be loaded into the memory simultaneously. To tackle these problems, we propose the first online learning framework for MTFS. A main advantage of the online algorithms is the efficiency in both time complexity and memory cost due to the closed-form solutions in updating the model weights at each iteration. Experimental results on a real-world dataset attest to the merits of the proposed algorithms.