Machine Learning - Special issue on inductive transfer
A sparse representation for function approximation
Neural Computation
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Convex Optimization
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-task feature and kernel selection for SVMs
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
On Learning Vector-Valued Functions
Neural Computation
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)
Bounds for Linear Multi-Task Learning
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Optimal Rates for the Regularized Least-Squares Algorithm
Foundations of Computational Mathematics
Marketing Research With Spss 14.0
Marketing Research With Spss 14.0
A model of inductive bias learning
Journal of Artificial Intelligence Research
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning a meta-level prior for feature relevance from multiple related tasks
Proceedings of the 24th international conference on 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
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
Semi-Supervised Multi-Task Regression
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning relevant eye movement feature spaces across users
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
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
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
A novel learning approach to multiple tasks based on boosting methodology
Pattern Recognition Letters
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
Concept classification with Bayesian multi-task learning
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
N-best reranking by multitask learning
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Online learning for multi-task feature selection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
Multitask Sparsity via Maximum Entropy Discrimination
The Journal of Machine Learning Research
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
The Journal of Machine Learning Research
Improving accuracy of microarray classification by a simple multi-task feature selection filter
International Journal of Data Mining and Bioinformatics
Limitations of matrix completion via trace norm minimization
ACM SIGKDD Explorations Newsletter
Neurocomputing
Sparse kernel regression for traffic flow forecasting
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Union Support Recovery in Multi-task Learning
The Journal of Machine Learning Research
Linear discriminant dimensionality reduction
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Transfer learning with adaptive regularizers
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Inferring multiple graphical structures
Statistics and Computing
Multi-task regularization of generative similarity models
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Multi-score learning for affect recognition: the case of body postures
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Towards multi-semantic image annotation with graph regularized exclusive group lasso
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Convex and Network Flow Optimization for Structured Sparsity
The Journal of Machine Learning Research
Structured Variable Selection with Sparsity-Inducing Norms
The Journal of Machine Learning Research
Learning with Structured Sparsity
The Journal of Machine Learning Research
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-task low-rank metric learning based on common subspace
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A novel framework based on trace norm minimization for audio event detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A Simpler Approach to Matrix Completion
The Journal of Machine Learning Research
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
A case study on meta-generalising: a Gaussian processes approach
The Journal of Machine Learning Research
Dimensionality reduction via compressive sensing
Pattern Recognition Letters
Joint feature selection and subspace learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Robust multi-task feature learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling disease progression via fused sparse group lasso
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Regularization techniques for learning with matrices
The Journal of Machine Learning Research
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
Tree ensembles for predicting structured outputs
Pattern Recognition
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
Hypergraph spectra for semi-supervised feature selection
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Multi-Task boosting by exploiting task relationships
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Semi-supervised multi-label classification: a simultaneous large-margin, subspace learning approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Sparse methods for biomedical data
ACM SIGKDD Explorations Newsletter
Max-margin embedding for multi-label learning
Pattern Recognition Letters
Multi-Task learning using shared and task specific information
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Transfer discriminant-analysis of canonical correlations for view-transfer action recognition
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Finite rank kernels for multi-task learning
Advances in Computational Mathematics
Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Learning with infinitely many features
Machine Learning
Regularizers for structured sparsity
Advances in Computational Mathematics
Multi-source learning with block-wise missing data for Alzheimer's disease prediction
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Recursive regularization for large-scale classification with hierarchical and graphical dependencies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable supervised dimensionality reduction using clustering
Proceedings of the 19th 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)
Multilabel relationship learning
ACM Transactions on Knowledge Discovery from Data (TKDD)
Variational inference in nonconjugate models
The Journal of Machine Learning Research
Multi-target regression with rule ensembles
The Journal of Machine Learning Research
Multi-task regression using minimal penalties
The Journal of Machine Learning Research
Learning output kernels for multi-task problems
Neurocomputing
Geometry preserving multi-task metric learning
Machine Learning
Co-regularized ensemble for feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multiple task learning using iteratively reweighted least square
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Adaptive error-correcting output codes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Audio classification with low-rank matrix representation features
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Proceedings of the 7th ACM international conference on Web search and data mining
Multi-stage multi-task feature learning
The Journal of Machine Learning Research
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
Multi-task learning with one-class SVM
Neurocomputing
Kernel regression with sparse metric learning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A joint convex penalty for inverse covariance matrix estimation
Computational Statistics & Data Analysis
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We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select--not learn--a few common variables across the tasks.