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Neural networks and the bias/variance dilemma
Neural Computation
Information-based objective functions for active data selection
Neural Computation
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning - Special issue on inductive transfer
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
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Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Convex Optimization
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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Proceedings of the 10th international conference on Intelligent user interfaces
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ACM Transactions on Information Systems (TOIS)
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
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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
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
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Active learning for logistic regression: an evaluation
Machine Learning
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Proceedings of the 25th international conference on Machine learning
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Expectation Propagation for Rating Players in Sports Competitions
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Sequential optimal design of neurophysiology experiments
Neural Computation
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Active learning with statistical models
Journal of Artificial Intelligence Research
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
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Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Preference Learning
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UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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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
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This paper presents a framework for optimizing the preference learning process. In many real-world applications in which preference learning is involved the available training data is scarce and obtaining labeled training data is expensive. Fortunately in many of the preference learning situations data is available from multiple subjects. We use the multi-task formalism to enhance the individual training data by making use of the preference information learned from other subjects. Furthermore, since obtaining labels is expensive, we optimally choose which data to ask a subject for labelling to obtain the most of information about her/his preferences. This paradigm--called active learning--has hardly been studied in a multi-task formalism. We propose an alternative for the standard criteria in active learning which actively chooses queries by making use of the available preference data from other subjects. The advantage of this alternative is the reduced computation costs and reduced time subjects are involved. We validate empirically our approach on three real-world data sets involving the preferences of people.