Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Incentive compatible regression learning
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Learning to classify with missing and corrupted features
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
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
Tight bounds for strategyproof classification
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Algorithms for strategyproof classification
Artificial Intelligence
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
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We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious. We present an algorithm, built on the SVM framework, that explicitly attempts to cope with low-quality and malicious teachers by decreasing their influence on the learning process. Our algorithm does not receive any prior information on the teachers, nor does it resort to repeated labeling (where each example is labeled by multiple teachers). We provide a theoretical analysis of our algorithm and demonstrate its merits empirically. Finally, we present a second algorithm with promising empirical results but without a formal analysis.