Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
Machine Learning - Special issue on inductive transfer
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Convex Optimization
RCV1: A New Benchmark Collection for Text Categorization Research
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
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Uncovering shared structures in multiclass classification
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
Convex multi-task feature learning
Machine Learning
A model of inductive bias learning
Journal of Artificial Intelligence Research
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Multi-task clustering via domain adaptation
Pattern Recognition
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Modeling disease progression via fused sparse group lasso
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient online learning for multitask feature selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multiple task learning using iteratively reweighted least square
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
User demographics prediction based on mobile data
Pervasive and Mobile Computing
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
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Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared structures from multiple related tasks. We present an improved formulation (iASO) for multi-task learning based on the non-convex alternating structure optimization (ASO) algorithm, in which all tasks are related by a shared feature representation. We convert iASO, a non-convex formulation, into a relaxed convex one, which is, however, not scalable to large data sets due to its complex constraints. We propose an alternating optimization (cASO) algorithm which solves the convex relaxation efficiently, and further show that cASO converges to a global optimum. In addition, we present a theoretical condition, under which cASO can find a globally optimal solution to iASO. Experiments on several benchmark data sets confirm our theoretical analysis.