Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Machine Learning - Special issue on inductive transfer
Learning to learn
An introduction to variational methods for graphical models
Learning in graphical models
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
A nonparametric hierarchical bayesian framework for information filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
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
A model of inductive bias learning
Journal of Artificial Intelligence Research
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
Proceedings of the 24th international conference on Machine learning
The matrix stick-breaking process for flexible multi-task learning
Proceedings of the 24th international conference on Machine learning
Hierarchical kernel stick-breaking process for multi-task image analysis
Proceedings of the 25th international conference on Machine learning
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Learning from Relevant Tasks Only
ECML '07 Proceedings of the 18th European 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
Kernel-Based Inductive Transfer
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Convex multi-task feature learning
Machine Learning
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
DS '08 Proceedings of the 11th International Conference on Discovery Science
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-Supervised Multi-Task Regression
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Hierarchical Bayesian domain adaptation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Transfer learning using task-level features with application to information retrieval
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
Bayesian multitask learning with latent hierarchies
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
A novel learning approach to multiple tasks based on boosting methodology
Pattern Recognition Letters
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
Classification with Incomplete Data Using Dirichlet Process Priors
The Journal of Machine Learning Research
Multitask Sparsity via Maximum Entropy Discrimination
The Journal of Machine Learning Research
Hierarchical text classification with latent concepts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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
Computationally Efficient Convolved Multiple Output Gaussian Processes
The Journal of Machine Learning Research
Multi-task clustering via domain adaptation
Pattern Recognition
Focused multi-task learning using gaussian processes
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Active supervised domain adaptation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Multi-task learning to rank for web search
Pattern Recognition Letters
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)
Leveraging Auxiliary Data for Learning to Rank
ACM Transactions on Intelligent Systems and Technology (TIST)
Expert Systems with Applications: An International Journal
Classification of the action surface EMG signals based on the dirichlet process mixtures method
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
A case study on meta-generalising: a Gaussian processes approach
The Journal of Machine Learning Research
Robust multi-task feature learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
Relational Feature Mining with Hierarchical Multitask kFOIL
Fundamenta Informaticae - Machine Learning in Bioinformatics
Efficient training of graph-regularized multitask SVMs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Learning multiple tasks with boosted decision trees
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
Multi-Task learning using shared and task specific information
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Semi-supervised multitask learning via self-training and maximum entropy discrimination
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
Learning output kernels for multi-task problems
Neurocomputing
Multiple task learning using iteratively reweighted least square
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Learning high-order task relationships in multi-task learning
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
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Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. In such a multi-task learning (MTL) scenario, it is necessary to identify groups of similar tasks that should be learned jointly. Relying on a Dirichlet process (DP) based statistical model to learn the extent of similarity between classification tasks, we develop computationally efficient algorithms for two different forms of the MTL problem. First, we consider a symmetric multi-task learning (SMTL) situation in which classifiers for multiple tasks are learned jointly using a variational Bayesian (VB) algorithm. Second, we consider an asymmetric multi-task learning (AMTL) formulation in which the posterior density function from the SMTL model parameters (from previous tasks) is used as a prior for a new task: this approach has the significant advantage of not requiring storage and use of all previous data from prior tasks. The AMTL formulation is solved with a simple Markov Chain Monte Carlo (MCMC) construction. Experimental results on two real life MTL problems indicate that the proposed algorithms: (a) automatically identify subgroups of related tasks whose training data appear to be drawn from similar distributions; and (b) are more accurate than simpler approaches such as single-task learning, pooling of data across all tasks, and simplified approximations to DP.