Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Machine Learning - Special issue on inductive transfer
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Review of "WordNet: an electronic lexical database" by Christiane Fellbaum. The MIT Press 1998.
Computational Linguistics
Multiple kernel learning, conic duality, and the SMO algorithm
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
Verbnet: a broad-coverage, comprehensive verb lexicon
Verbnet: a broad-coverage, comprehensive verb lexicon
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
An empirical study of the behavior of active learning for word sense disambiguation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Learning a meta-level prior for feature relevance from multiple related tasks
Proceedings of the 24th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
Finding Cohesive Clusters for Analyzing Knowledge Communities
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Convex multi-task feature learning
Machine Learning
Efficient Feature Selection in the Presence of Multiple Feature Classes
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning with structured sparsity
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On the Consistency of Feature Selection using Greedy Least Squares Regression
The Journal of Machine Learning Research
Applying alternating structure optimization to word sense disambiguation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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
Transfer learning, feature selection and word sense disambguation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
IEEE Transactions on Information Theory
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Efficient online learning for multitask feature selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an elegant way of incorporating arbitrary sparsity patterns in the feature space by using two-part MDL coding schemes. We present MIC based models for the problems of grouped feature selection (MIC-GROUP) and multi-task feature selection (MIC-MULTI). MIC-GROUP assumes that the features are divided into groups and induces two level sparsity, selecting a subset of the feature groups, and also selecting features within each selected group. MIC-MULTI applies when there are multiple related tasks that share the same set of potentially predictive features. It also induces two level sparsity, selecting a subset of the features, and then selecting which of the tasks each feature should be added to. Lastly, we propose a model, TRANSFEAT, that can be used to transfer knowledge from a set of previously learned tasks to a new task that is expected to share similar features. All three methods are designed for selecting a small set of predictive features from a large pool of candidate features. We demonstrate the effectiveness of our approach with experimental results on data from genomics and from word sense disambiguation problems.