Communications of the ACM
The use of knowledge in analogy and induction
The use of knowledge in analogy and induction
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Symbolic-neural systems and the use of hints for developing complex systems
International Journal of Man-Machine Studies
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Learning internal representations
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Learning to learn
The canonical distortion measure in feature space and 1-NN classification
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
The Canonical Distortion Measure for Vector Quantization and Function Approximation
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discriminability-Based Transfer between Neural Networks
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A Method for Learning From Hints
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Rule-Injection Hints as a Means of Improving Network Performance and Learning Time
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Repeat Learning Using Predicate Invention
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Information Theory
A perspective view and survey of meta-learning
Artificial Intelligence Review
A theoretical framework for learning from a pool of disparate data sources
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-training: learning probabilistic mappings between topics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Estimating relatedness via data compression
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
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Hierarchical maximum entropy density estimation
Proceedings of the 24th international conference on Machine learning
Journal of Biomedical Informatics
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
Learning Similarity with Operator-valued Large-margin Classifiers
The Journal of Machine Learning Research
A Hierarchical Approach for Multi-task Logistic Regression
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
On Universal Transfer Learning
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
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
A Uniform Lower Error Bound for Half-Space Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Convex multi-task feature learning
Machine Learning
Flexible latent variable models for multi-task learning
Machine Learning
A Multitask Learning Approach to Face Recognition Based on Neural Networks
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
On universal transfer learning
Theoretical Computer Science
Transfer bounds for linear feature learning
Machine Learning
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A value theory of meta-learning algorithms
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
An Information-Theoretic Approach for Multi-task Learning
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Transferring multi-device localization models using latent multi-task learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Generative prior knowledge for discriminative classification
Journal of Artificial Intelligence Research
Efficient Bayesian task-level transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Signal Processing
Boosted online learning for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Evolution and hypercomputing in global distributed evolvable virtual machines environment
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Multitask learning with expert advice
COLT'07 Proceedings of the 20th annual conference on Learning theory
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
Multitask Sparsity via Maximum Entropy Discrimination
The Journal of Machine Learning Research
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
Learning multiple metrics for ranking
Frontiers of Computer Science in China
Inferring multiple graphical structures
Statistics and Computing
Conditional graphical models for protein structure prediction
Conditional graphical models for protein structure prediction
Multi-platform gene-expression mining and marker gene analysis
International Journal of Data Mining and Bioinformatics
Shared feature extraction for semi-supervised image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Shared feature extraction for semi-supervised image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The rademacher complexity of linear transformation classes
COLT'06 Proceedings of the 19th annual conference on Learning Theory
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A self-organising, self-adaptable cellular system
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Efficient case based feature construction
ECML'05 Proceedings of the 16th European conference on Machine Learning
Ensemble learning based on multi-task class labels
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Exploiting multitask learning schemes using private subnetworks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Generalization bounds for subspace selection and hyperbolic PCA
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
A case study on meta-generalising: a Gaussian processes approach
The Journal of Machine Learning Research
Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Robust multi-task feature learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Tree ensembles for predicting structured outputs
Pattern Recognition
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
Hierarchical training of multiple SVMs for personalized web filtering
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Multitask twin support vector machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
A theory of transfer learning with applications to active learning
Machine Learning
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.