GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Topics in matrix analysis
Machine Learning - Special issue on inductive transfer
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
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
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
On Learning Vector-Valued Functions
Neural Computation
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Nonparametric identification of population models via Gaussian processes
Automatica (Journal of IFAC)
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Robust multi-task learning with t-processes
Proceedings of the 24th international conference on Machine learning
Multi-task compressive sensing with Dirichlet process priors
Proceedings of the 25th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
Flexible latent variable models for multi-task learning
Machine Learning
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
Bayesian Online Multitask Learning of Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conic Programming for Multitask Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Shift-invariant grouped multi-task learning for Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Client–Server Multitask Learning From Distributed Datasets
IEEE Transactions on Neural Networks
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
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Simultaneously solving multiple related learning tasks is beneficial under a variety of circumstances, but the prior knowledge necessary to properly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based multi-task learning technique that automatically reveals structural inter-task relationships. Building over the framework of output kernel learning (OKL), we introduce a method that jointly learns multiple functions and a low-rank multi-task kernel by solving a non-convex regularization problem. Optimization is carried out via a block coordinate descent strategy, where each subproblem is solved using suitable conjugate gradient (CG) type iterative methods for linear operator equations. The effectiveness of the proposed approach is demonstrated on pharmacological and collaborative filtering data.