Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
The weighted majority algorithm
Information and Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Some label efficient learning results
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Prediction, Learning, and Games
Prediction, Learning, and Games
ICML '06 Proceedings of the 23rd international conference on Machine learning
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
Logarithmic regret algorithms for online convex optimization
Machine Learning
Knows what it knows: a framework for self-aware learning
Proceedings of the 25th international conference on Machine learning
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Multiple Sources
The Journal of Machine Learning Research
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Robust bounds for classification via selective sampling
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Good learners for evil teachers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Automatic weight learning for multiple data sources when learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
COLT'07 Proceedings of the 20th annual conference on Learning theory
Multiple source adaptation and the Rényi divergence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
Rademacher Complexities and Bounding the Excess Risk in Active Learning
The Journal of Machine Learning Research
Learning from multiple annotators with Gaussian processes
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Analysis of perceptron-based active learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Minimizing regret with label efficient prediction
IEEE Transactions on Information Theory
Improved Risk Tail Bounds for On-Line Algorithms
IEEE Transactions on Information Theory
Minimax Bounds for Active Learning
IEEE Transactions on Information Theory
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We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of our algorithm and on the number of labels it queries when faced with an adaptive adversarial strategy of generating the instances. Our bounds both generalize and strictly improve over previous bounds in similar settings. Additionally, our selective sampling algorithm can be converted into an efficient statistical active learning algorithm. We extend our algorithm and analysis to the multiple-teacher setting, where the algorithm can choose which subset of teachers to query for each label. Finally, we demonstrate the effectiveness of our techniques on a real-world Internet search problem.