Matrix computations (3rd ed.)
Machine Learning - Special issue on inductive transfer
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Convex Optimization
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Excessive Gap Technique in Nonsmooth Convex Minimization
SIAM Journal on Optimization
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Fast maximum margin matrix factorization for collaborative prediction
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
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Improving maximum margin matrix factorization
Machine Learning
An Algorithm for Transfer Learning in a Heterogeneous Environment
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Convex multi-task feature learning
Machine Learning
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Drosophila gene expression pattern annotation using sparse features and term-term interactions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
A model of inductive bias learning
Journal of Artificial Intelligence Research
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Mathematical Programming: Series A and B
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Optimal exact least squares rank minimization
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust multi-task feature learning
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
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We consider a recently proposed optimization formulation of multi-task learning based on trace norm regularized least squares. While this problem may be formulated as a semidefinite program (SDP), its size is beyond general SDP solvers. Previous solution approaches apply proximal gradient methods to solve the primal problem. We derive new primal and dual reformulations of this problem, including a reduced dual formulation that involves minimizing a convex quadratic function over an operator-norm ball in matrix space. This reduced dual problem may be solved by gradient-projection methods, with each projection involving a singular value decomposition. The dual approach is compared with existing approaches and its practical effectiveness is illustrated on simulations and an application to gene expression pattern analysis.